Overview

Brought to you by YData

Dataset statistics

Number of variables59
Number of observations40000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.0 MiB
Average record size in memory472.0 B

Variable types

Text3
DateTime6
Numeric24
Categorical24
Boolean2

Alerts

Gender has constant value "0" Constant
Check_Point has constant value "False" Constant
Accident_Severity is highly overall correlated with Fraud_IndHigh correlation
Accident_Type is highly overall correlated with Collision_Type and 1 other fieldsHigh correlation
Annual_Mileage is highly overall correlated with Low_Mileage_DiscountHigh correlation
Auto_Year is highly overall correlated with Claim_Intensity and 1 other fieldsHigh correlation
Claim_Intensity is highly overall correlated with Auto_Year and 5 other fieldsHigh correlation
Collision_Type is highly overall correlated with Accident_TypeHigh correlation
Fraud_Ind is highly overall correlated with Accident_SeverityHigh correlation
Indemnity_Policy_BI is highly overall correlated with Policy_BI and 1 other fieldsHigh correlation
Injury_Claim is highly overall correlated with Claim_Intensity and 1 other fieldsHigh correlation
Low_Mileage_Discount is highly overall correlated with Annual_MileageHigh correlation
Num_of_Vehicles_Involved is highly overall correlated with Accident_TypeHigh correlation
Policy_BI is highly overall correlated with Indemnity_Policy_BI and 1 other fieldsHigh correlation
Property_Claim is highly overall correlated with Claim_Intensity and 1 other fieldsHigh correlation
Total_Claim is highly overall correlated with Claim_Intensity and 3 other fieldsHigh correlation
Vehicle_Claim is highly overall correlated with Claim_Intensity and 1 other fieldsHigh correlation
Vehicle_Cost is highly overall correlated with Auto_Year and 1 other fieldsHigh correlation
Wait_Policy_BI is highly overall correlated with Indemnity_Policy_BI and 1 other fieldsHigh correlation
Garage_Location is highly imbalanced (94.7%) Imbalance
Commute_Discount is highly imbalanced (86.1%) Imbalance
Vehicle_Cost is uniformly distributed Uniform
Claim_ID has unique values Unique
Vehicle_Registration has unique values Unique
Claim_Intensity has unique values Unique
Umbrella_Limit has 31960 (79.9%) zeros Zeros
Education has 5801 (14.5%) zeros Zeros
Capital_Gains has 20249 (50.6%) zeros Zeros
Capital_Loss has 19043 (47.6%) zeros Zeros
Accident_Hour has 2044 (5.1%) zeros Zeros
Auto_Make has 2713 (6.8%) zeros Zeros
Auto_Model has 697 (1.7%) zeros Zeros
Auto_Year has 4326 (10.8%) zeros Zeros
Vehicle_Color has 9173 (22.9%) zeros Zeros
Claim_Duration has 6271 (15.7%) zeros Zeros

Reproduction

Analysis started2025-07-25 13:51:09.055996
Analysis finished2025-07-25 13:52:04.840913
Duration55.78 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Claim_ID
Text

Unique 

Distinct40000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:05.049448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters400000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40000 ?
Unique (%)100.0%

Sample

1st rowAA00000001
2nd rowAA00000002
3rd rowAA00000003
4th rowAA00000004
5th rowAA00000005
ValueCountFrequency (%)
aa00039985 1
 
< 0.1%
aa00039986 1
 
< 0.1%
aa00039987 1
 
< 0.1%
aa00039988 1
 
< 0.1%
aa00039989 1
 
< 0.1%
aa00039990 1
 
< 0.1%
aa00039991 1
 
< 0.1%
aa00039992 1
 
< 0.1%
aa00039993 1
 
< 0.1%
aa00039994 1
 
< 0.1%
Other values (39990) 39990
> 99.9%
2025-07-25T19:22:05.276870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 145999
36.5%
A 80000
20.0%
3 26000
 
6.5%
2 26000
 
6.5%
1 26000
 
6.5%
4 16001
 
4.0%
8 16000
 
4.0%
9 16000
 
4.0%
7 16000
 
4.0%
6 16000
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 145999
36.5%
A 80000
20.0%
3 26000
 
6.5%
2 26000
 
6.5%
1 26000
 
6.5%
4 16001
 
4.0%
8 16000
 
4.0%
9 16000
 
4.0%
7 16000
 
4.0%
6 16000
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 145999
36.5%
A 80000
20.0%
3 26000
 
6.5%
2 26000
 
6.5%
1 26000
 
6.5%
4 16001
 
4.0%
8 16000
 
4.0%
9 16000
 
4.0%
7 16000
 
4.0%
6 16000
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 145999
36.5%
A 80000
20.0%
3 26000
 
6.5%
2 26000
 
6.5%
1 26000
 
6.5%
4 16001
 
4.0%
8 16000
 
4.0%
9 16000
 
4.0%
7 16000
 
4.0%
6 16000
 
4.0%
Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2022-01-01 00:00:00
Maximum2023-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T19:22:05.333954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:05.405518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

Customer_Life_Value1
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.999275
Minimum12
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:05.475642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q115
median18
Q321
95-th percentile24
Maximum24
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7454806
Coefficient of variation (CV)0.20809064
Kurtosis-1.2173609
Mean17.999275
Median Absolute Deviation (MAD)3
Skewness0.00046700601
Sum719971
Variance14.028625
MonotonicityNot monotonic
2025-07-25T19:22:05.543302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
16 3148
 
7.9%
18 3121
 
7.8%
21 3113
 
7.8%
14 3108
 
7.8%
12 3107
 
7.8%
15 3102
 
7.8%
24 3090
 
7.7%
23 3075
 
7.7%
20 3074
 
7.7%
22 3065
 
7.7%
Other values (3) 8997
22.5%
ValueCountFrequency (%)
12 3107
7.8%
13 3024
7.6%
14 3108
7.8%
15 3102
7.8%
16 3148
7.9%
17 2934
7.3%
18 3121
7.8%
19 3039
7.6%
20 3074
7.7%
21 3113
7.8%
ValueCountFrequency (%)
24 3090
7.7%
23 3075
7.7%
22 3065
7.7%
21 3113
7.8%
20 3074
7.7%
19 3039
7.6%
18 3121
7.8%
17 2934
7.3%
16 3148
7.9%
15 3102
7.8%

Age_Insured
Real number (ℝ)

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.94535
Minimum19
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:05.629396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile26
Q132
median38
Q344
95-th percentile57
Maximum64
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.1349603
Coefficient of variation (CV)0.23455843
Kurtosis-0.25047864
Mean38.94535
Median Absolute Deviation (MAD)6
Skewness0.48571181
Sum1557814
Variance83.4475
MonotonicityNot monotonic
2025-07-25T19:22:05.969854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
43 1972
 
4.9%
39 1955
 
4.9%
41 1785
 
4.5%
34 1751
 
4.4%
31 1712
 
4.3%
30 1687
 
4.2%
38 1679
 
4.2%
37 1661
 
4.2%
40 1585
 
4.0%
33 1542
 
3.9%
Other values (36) 22671
56.7%
ValueCountFrequency (%)
19 37
 
0.1%
20 37
 
0.1%
21 240
 
0.6%
22 40
 
0.1%
23 256
 
0.6%
24 403
 
1.0%
25 628
1.6%
26 1014
2.5%
27 907
2.3%
28 1206
3.0%
ValueCountFrequency (%)
64 96
 
0.2%
63 69
 
0.2%
62 160
 
0.4%
61 432
1.1%
60 357
0.9%
59 200
 
0.5%
58 287
0.7%
57 623
1.6%
56 313
0.8%
55 588
1.5%

Policy_Num
Real number (ℝ)

Distinct1000
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5501353 × 108
Minimum1.111789 × 108
Maximum1.9994753 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:06.067497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.111789 × 108
5-th percentile1.1614359 × 108
Q11.3169756 × 108
median1.5461678 × 108
Q31.7795754 × 108
95-th percentile1.953107 × 108
Maximum1.9994753 × 108
Range88768623
Interquartile range (IQR)46259987

Descriptive statistics

Standard deviation25999828
Coefficient of variation (CV)0.16772619
Kurtosis-1.2910867
Mean1.5501353 × 108
Median Absolute Deviation (MAD)23186045
Skewness0.052351151
Sum6.2005411 × 1012
Variance6.7599107 × 1014
MonotonicityNot monotonic
2025-07-25T19:22:06.157537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127845548 61
 
0.2%
179715851 59
 
0.1%
154616785 59
 
0.1%
133661485 58
 
0.1%
135463054 57
 
0.1%
123000811 56
 
0.1%
170154438 56
 
0.1%
199665782 56
 
0.1%
185295646 55
 
0.1%
178610842 55
 
0.1%
Other values (990) 39428
98.6%
ValueCountFrequency (%)
111178904 50
0.1%
111297648 46
0.1%
111424913 31
0.1%
111441676 46
0.1%
111551060 44
0.1%
111823129 39
0.1%
112117835 29
0.1%
112120203 40
0.1%
112190907 33
0.1%
112265654 43
0.1%
ValueCountFrequency (%)
199947527 46
0.1%
199930219 39
0.1%
199815585 30
0.1%
199793575 52
0.1%
199704644 39
0.1%
199665782 56
0.1%
199511065 38
0.1%
199485035 39
0.1%
199133595 41
0.1%
199046488 45
0.1%

Policy_State
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
14126 
1
13300 
2
12574 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 14126
35.3%
1 13300
33.2%
2 12574
31.4%

Length

2025-07-25T19:22:06.237682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:06.294019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14126
35.3%
1 13300
33.2%
2 12574
31.4%

Most occurring characters

ValueCountFrequency (%)
0 14126
35.3%
1 13300
33.2%
2 12574
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14126
35.3%
1 13300
33.2%
2 12574
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14126
35.3%
1 13300
33.2%
2 12574
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14126
35.3%
1 13300
33.2%
2 12574
31.4%
Distinct112
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2023-07-01 00:00:00
Maximum2023-11-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T19:22:06.363724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:06.464070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct113
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2024-01-01 00:00:00
Maximum2024-05-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T19:22:06.563347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:06.673585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Policy_BI
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
250/500
14047 
100/300
13962 
500/1000
11991 

Length

Max length8
Median length7
Mean length7.299775
Min length7

Characters and Unicode

Total characters291991
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row500/1000
2nd row250/500
3rd row500/1000
4th row500/1000
5th row250/500

Common Values

ValueCountFrequency (%)
250/500 14047
35.1%
100/300 13962
34.9%
500/1000 11991
30.0%

Length

2025-07-25T19:22:06.758188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:06.810678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
250/500 14047
35.1%
100/300 13962
34.9%
500/1000 11991
30.0%

Most occurring characters

ValueCountFrequency (%)
0 157944
54.1%
5 40085
 
13.7%
/ 40000
 
13.7%
1 25953
 
8.9%
2 14047
 
4.8%
3 13962
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 157944
54.1%
5 40085
 
13.7%
/ 40000
 
13.7%
1 25953
 
8.9%
2 14047
 
4.8%
3 13962
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 157944
54.1%
5 40085
 
13.7%
/ 40000
 
13.7%
1 25953
 
8.9%
2 14047
 
4.8%
3 13962
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 157944
54.1%
5 40085
 
13.7%
/ 40000
 
13.7%
1 25953
 
8.9%
2 14047
 
4.8%
3 13962
 
4.8%

Policy_Ded
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
1000
14072 
500
13681 
2000
12247 

Length

Max length4
Median length4
Mean length3.657975
Min length3

Characters and Unicode

Total characters146319
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row500
3rd row500
4th row1000
5th row1000

Common Values

ValueCountFrequency (%)
1000 14072
35.2%
500 13681
34.2%
2000 12247
30.6%

Length

2025-07-25T19:22:06.886309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:06.933875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1000 14072
35.2%
500 13681
34.2%
2000 12247
30.6%

Most occurring characters

ValueCountFrequency (%)
0 106319
72.7%
1 14072
 
9.6%
5 13681
 
9.4%
2 12247
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 146319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 106319
72.7%
1 14072
 
9.6%
5 13681
 
9.4%
2 12247
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 146319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 106319
72.7%
1 14072
 
9.6%
5 13681
 
9.4%
2 12247
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 146319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 106319
72.7%
1 14072
 
9.6%
5 13681
 
9.4%
2 12247
 
8.4%

Policy_Premium
Real number (ℝ)

Distinct991
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1255.9668
Minimum433.33
Maximum2047.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:07.004480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum433.33
5-th percentile847.03
Q11086.21
median1259.02
Q31418.5
95-th percentile1655.79
Maximum2047.59
Range1614.26
Interquartile range (IQR)332.29

Descriptive statistics

Standard deviation245.68388
Coefficient of variation (CV)0.19561335
Kurtosis0.038683109
Mean1255.9668
Median Absolute Deviation (MAD)164.32
Skewness-0.0096481834
Sum50238672
Variance60360.568
MonotonicityNot monotonic
2025-07-25T19:22:07.104690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1374.22 96
 
0.2%
1074.07 93
 
0.2%
1524.45 87
 
0.2%
1558.29 85
 
0.2%
1073.83 83
 
0.2%
1281.25 72
 
0.2%
1362.87 72
 
0.2%
1389.13 70
 
0.2%
1215.36 67
 
0.2%
1005.47 61
 
0.2%
Other values (981) 39214
98.0%
ValueCountFrequency (%)
433.33 39
0.1%
484.67 36
0.1%
538.17 42
0.1%
566.11 50
0.1%
617.11 49
0.1%
625.08 43
0.1%
653.66 41
0.1%
664.86 39
0.1%
671.01 40
0.1%
671.92 49
0.1%
ValueCountFrequency (%)
2047.59 39
0.1%
1969.63 45
0.1%
1935.85 43
0.1%
1927.87 29
0.1%
1922.84 42
0.1%
1896.91 40
0.1%
1878.44 41
0.1%
1865.83 39
0.1%
1863.04 45
0.1%
1861.43 44
0.1%

Umbrella_Limit
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1094800
Minimum0
Maximum10000000
Zeros31960
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:07.211401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6000000
Maximum10000000
Range10000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2282606.7
Coefficient of variation (CV)2.0849531
Kurtosis1.7558994
Mean1094800
Median Absolute Deviation (MAD)0
Skewness1.8032284
Sum4.3792 × 1010
Variance5.2102932 × 1012
MonotonicityNot monotonic
2025-07-25T19:22:07.285612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 31960
79.9%
6000000 2224
 
5.6%
5000000 1877
 
4.7%
4000000 1588
 
4.0%
7000000 1183
 
3.0%
3000000 443
 
1.1%
8000000 321
 
0.8%
9000000 177
 
0.4%
2000000 119
 
0.3%
10000000 66
 
0.2%
ValueCountFrequency (%)
0 31960
79.9%
1000000 42
 
0.1%
2000000 119
 
0.3%
3000000 443
 
1.1%
4000000 1588
 
4.0%
5000000 1877
 
4.7%
6000000 2224
 
5.6%
7000000 1183
 
3.0%
8000000 321
 
0.8%
9000000 177
 
0.4%
ValueCountFrequency (%)
10000000 66
 
0.2%
9000000 177
 
0.4%
8000000 321
 
0.8%
7000000 1183
3.0%
6000000 2224
5.6%
5000000 1877
4.7%
4000000 1588
4.0%
3000000 443
 
1.1%
2000000 119
 
0.3%
1000000 42
 
0.1%

Insured_Zip
Real number (ℝ)

Distinct995
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501415.97
Minimum430104
Maximum620962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:07.375233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum430104
5-th percentile433275
Q1448436
median466498
Q3603251
95-th percentile617463.35
Maximum620962
Range190858
Interquartile range (IQR)154815

Descriptive statistics

Standard deviation71712.899
Coefficient of variation (CV)0.14302077
Kurtosis-1.2001256
Mean501415.97
Median Absolute Deviation (MAD)21940
Skewness0.80953764
Sum2.0056639 × 1010
Variance5.1427399 × 109
MonotonicityNot monotonic
2025-07-25T19:22:07.475396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477695 92
 
0.2%
446895 84
 
0.2%
431202 80
 
0.2%
456602 71
 
0.2%
469429 69
 
0.2%
441871 61
 
0.2%
600702 59
 
0.1%
477268 59
 
0.1%
434733 58
 
0.1%
619570 57
 
0.1%
Other values (985) 39310
98.3%
ValueCountFrequency (%)
430104 40
0.1%
430141 47
0.1%
430232 40
0.1%
430380 43
0.1%
430567 43
0.1%
430621 39
0.1%
430632 49
0.1%
430665 37
0.1%
430714 54
0.1%
430832 40
0.1%
ValueCountFrequency (%)
620962 46
0.1%
620869 37
0.1%
620819 48
0.1%
620757 42
0.1%
620737 38
0.1%
620507 54
0.1%
620493 28
0.1%
620473 46
0.1%
620358 34
0.1%
620207 34
0.1%

Gender
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
40000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40000
100.0%

Length

2025-07-25T19:22:07.571121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:07.617690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 40000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 40000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 40000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 40000
100.0%

Education
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.953825
Minimum0
Maximum6
Zeros5801
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:07.664768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9457486
Coefficient of variation (CV)0.65872168
Kurtosis-1.1666499
Mean2.953825
Median Absolute Deviation (MAD)2
Skewness0.0087371634
Sum118153
Variance3.7859375
MonotonicityNot monotonic
2025-07-25T19:22:07.720562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 6443
16.1%
2 6405
16.0%
0 5801
14.5%
5 5721
14.3%
4 5713
14.3%
1 4989
12.5%
6 4928
12.3%
ValueCountFrequency (%)
0 5801
14.5%
1 4989
12.5%
2 6405
16.0%
3 6443
16.1%
4 5713
14.3%
5 5721
14.3%
6 4928
12.3%
ValueCountFrequency (%)
6 4928
12.3%
5 5721
14.3%
4 5713
14.3%
3 6443
16.1%
2 6405
16.0%
1 4989
12.5%
0 5801
14.5%

Occupation
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
machine-op-inspct
3763 
prof-specialty
3391 
craft-repair
3080 
sales
3010 
tech-support
2988 
Other values (9)
23768 

Length

Max length17
Median length16
Mean length13.533475
Min length5

Characters and Unicode

Total characters541339
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadm-clerical
2nd rowprotective-serv
3rd rowpriv-house-serv
4th rowtech-support
5th rowexec-managerial

Common Values

ValueCountFrequency (%)
machine-op-inspct 3763
 
9.4%
prof-specialty 3391
 
8.5%
craft-repair 3080
 
7.7%
sales 3010
 
7.5%
tech-support 2988
 
7.5%
exec-managerial 2970
 
7.4%
priv-house-serv 2869
 
7.2%
other-service 2864
 
7.2%
transport-moving 2859
 
7.1%
armed-forces 2761
 
6.9%
Other values (4) 9445
23.6%

Length

2025-07-25T19:22:07.800397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct 3763
 
9.4%
prof-specialty 3391
 
8.5%
craft-repair 3080
 
7.7%
sales 3010
 
7.5%
tech-support 2988
 
7.5%
exec-managerial 2970
 
7.4%
priv-house-serv 2869
 
7.2%
other-service 2864
 
7.2%
transport-moving 2859
 
7.1%
armed-forces 2761
 
6.9%
Other values (4) 9445
23.6%

Most occurring characters

ValueCountFrequency (%)
e 61708
11.4%
r 55460
10.2%
- 43622
 
8.1%
a 42455
 
7.8%
s 39436
 
7.3%
i 37185
 
6.9%
c 35408
 
6.5%
p 31651
 
5.8%
t 29910
 
5.5%
o 26913
 
5.0%
Other values (11) 137591
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 541339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 61708
11.4%
r 55460
10.2%
- 43622
 
8.1%
a 42455
 
7.8%
s 39436
 
7.3%
i 37185
 
6.9%
c 35408
 
6.5%
p 31651
 
5.8%
t 29910
 
5.5%
o 26913
 
5.0%
Other values (11) 137591
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 541339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 61708
11.4%
r 55460
10.2%
- 43622
 
8.1%
a 42455
 
7.8%
s 39436
 
7.3%
i 37185
 
6.9%
c 35408
 
6.5%
p 31651
 
5.8%
t 29910
 
5.5%
o 26913
 
5.0%
Other values (11) 137591
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 541339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 61708
11.4%
r 55460
10.2%
- 43622
 
8.1%
a 42455
 
7.8%
s 39436
 
7.3%
i 37185
 
6.9%
c 35408
 
6.5%
p 31651
 
5.8%
t 29910
 
5.5%
o 26913
 
5.0%
Other values (11) 137591
25.4%

Hobbies
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
reading
 
2619
exercise
 
2282
bungie-jumping
 
2263
paintball
 
2252
movies
 
2237
Other values (15)
28347 

Length

Max length14
Median length11
Mean length8.110075
Min length4

Characters and Unicode

Total characters324403
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpolo
2nd rowmovies
3rd rowboard-games
4th rowreading
5th rowcamping

Common Values

ValueCountFrequency (%)
reading 2619
 
6.5%
exercise 2282
 
5.7%
bungie-jumping 2263
 
5.7%
paintball 2252
 
5.6%
movies 2237
 
5.6%
camping 2207
 
5.5%
kayaking 2196
 
5.5%
golf 2141
 
5.4%
yachting 2106
 
5.3%
hiking 2054
 
5.1%
Other values (10) 17643
44.1%

Length

2025-07-25T19:22:07.879000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
reading 2619
 
6.5%
exercise 2282
 
5.7%
bungie-jumping 2263
 
5.7%
paintball 2252
 
5.6%
movies 2237
 
5.6%
camping 2207
 
5.5%
kayaking 2196
 
5.5%
golf 2141
 
5.4%
yachting 2106
 
5.3%
hiking 2054
 
5.1%
Other values (10) 17643
44.1%

Most occurring characters

ValueCountFrequency (%)
i 37129
 
11.4%
g 28980
 
8.9%
e 28269
 
8.7%
a 27900
 
8.6%
n 26778
 
8.3%
s 21843
 
6.7%
o 13577
 
4.2%
l 12828
 
4.0%
m 12580
 
3.9%
p 12193
 
3.8%
Other values (14) 102326
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 324403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 37129
 
11.4%
g 28980
 
8.9%
e 28269
 
8.7%
a 27900
 
8.6%
n 26778
 
8.3%
s 21843
 
6.7%
o 13577
 
4.2%
l 12828
 
4.0%
m 12580
 
3.9%
p 12193
 
3.8%
Other values (14) 102326
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 324403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 37129
 
11.4%
g 28980
 
8.9%
e 28269
 
8.7%
a 27900
 
8.6%
n 26778
 
8.3%
s 21843
 
6.7%
o 13577
 
4.2%
l 12828
 
4.0%
m 12580
 
3.9%
p 12193
 
3.8%
Other values (14) 102326
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 324403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 37129
 
11.4%
g 28980
 
8.9%
e 28269
 
8.7%
a 27900
 
8.6%
n 26778
 
8.3%
s 21843
 
6.7%
o 13577
 
4.2%
l 12828
 
4.0%
m 12580
 
3.9%
p 12193
 
3.8%
Other values (14) 102326
31.5%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
own-child
7343 
other-relative
7053 
not-in-family
6978 
husband
6758 
wife
6176 

Length

Max length14
Median length13
Mean length9.469525
Min length4

Characters and Unicode

Total characters378781
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother-relative
2nd rowhusband
3rd rowother-relative
4th rowown-child
5th rowhusband

Common Values

ValueCountFrequency (%)
own-child 7343
18.4%
other-relative 7053
17.6%
not-in-family 6978
17.4%
husband 6758
16.9%
wife 6176
15.4%
unmarried 5692
14.2%

Length

2025-07-25T19:22:07.965424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:08.026801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
own-child 7343
18.4%
other-relative 7053
17.6%
not-in-family 6978
17.4%
husband 6758
16.9%
wife 6176
15.4%
unmarried 5692
14.2%

Most occurring characters

ValueCountFrequency (%)
i 40220
 
10.6%
n 33749
 
8.9%
e 33027
 
8.7%
- 28352
 
7.5%
a 26481
 
7.0%
r 25490
 
6.7%
l 21374
 
5.6%
o 21374
 
5.6%
h 21154
 
5.6%
t 21084
 
5.6%
Other values (10) 106476
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 378781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 40220
 
10.6%
n 33749
 
8.9%
e 33027
 
8.7%
- 28352
 
7.5%
a 26481
 
7.0%
r 25490
 
6.7%
l 21374
 
5.6%
o 21374
 
5.6%
h 21154
 
5.6%
t 21084
 
5.6%
Other values (10) 106476
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 378781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 40220
 
10.6%
n 33749
 
8.9%
e 33027
 
8.7%
- 28352
 
7.5%
a 26481
 
7.0%
r 25490
 
6.7%
l 21374
 
5.6%
o 21374
 
5.6%
h 21154
 
5.6%
t 21084
 
5.6%
Other values (10) 106476
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 378781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 40220
 
10.6%
n 33749
 
8.9%
e 33027
 
8.7%
- 28352
 
7.5%
a 26481
 
7.0%
r 25490
 
6.7%
l 21374
 
5.6%
o 21374
 
5.6%
h 21154
 
5.6%
t 21084
 
5.6%
Other values (10) 106476
28.1%

Capital_Gains
Real number (ℝ)

Zeros 

Distinct338
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25113.682
Minimum0
Maximum100500
Zeros20249
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:08.127754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350800
95-th percentile70600
Maximum100500
Range100500
Interquartile range (IQR)50800

Descriptive statistics

Standard deviation27759.502
Coefficient of variation (CV)1.1053537
Kurtosis-1.2805004
Mean25113.682
Median Absolute Deviation (MAD)0
Skewness0.47275984
Sum1.0045473 × 109
Variance7.7058996 × 108
MonotonicityNot monotonic
2025-07-25T19:22:08.218422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20249
50.6%
46300 206
 
0.5%
68500 183
 
0.5%
44000 140
 
0.4%
45500 140
 
0.4%
51500 138
 
0.3%
46700 133
 
0.3%
59600 131
 
0.3%
36900 129
 
0.3%
58500 127
 
0.3%
Other values (328) 18424
46.1%
ValueCountFrequency (%)
0 20249
50.6%
800 41
 
0.1%
10000 46
 
0.1%
11000 36
 
0.1%
12100 46
 
0.1%
12800 43
 
0.1%
13100 33
 
0.1%
14100 47
 
0.1%
16100 48
 
0.1%
17300 40
 
0.1%
ValueCountFrequency (%)
100500 33
0.1%
98800 45
0.1%
94800 44
0.1%
91900 29
0.1%
90700 39
0.1%
88800 36
0.1%
88400 34
0.1%
87800 35
0.1%
84900 36
0.1%
83900 45
0.1%

Capital_Loss
Real number (ℝ)

Zeros 

Distinct354
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26754.395
Minimum-111100
Maximum0
Zeros19043
Zeros (%)47.6%
Negative20957
Negative (%)52.4%
Memory size312.6 KiB
2025-07-25T19:22:08.309769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-111100
5-th percentile-72100
Q1-51500
median-22400
Q30
95-th percentile0
Maximum0
Range111100
Interquartile range (IQR)51500

Descriptive statistics

Standard deviation28088.848
Coefficient of variation (CV)-1.0498779
Kurtosis-1.3114989
Mean-26754.395
Median Absolute Deviation (MAD)22400
Skewness-0.39283306
Sum-1.0701758 × 109
Variance7.889834 × 108
MonotonicityNot monotonic
2025-07-25T19:22:08.403014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19043
47.6%
-31700 217
 
0.5%
-53700 191
 
0.5%
-50300 181
 
0.5%
-49200 176
 
0.4%
-32800 174
 
0.4%
-53800 169
 
0.4%
-61400 161
 
0.4%
-51000 153
 
0.4%
-58400 151
 
0.4%
Other values (344) 19384
48.5%
ValueCountFrequency (%)
-111100 44
0.1%
-93600 45
0.1%
-91400 29
0.1%
-91200 42
0.1%
-90600 48
0.1%
-90200 30
0.1%
-90100 34
0.1%
-89400 37
0.1%
-88300 41
0.1%
-87300 36
0.1%
ValueCountFrequency (%)
0 19043
47.6%
-5700 26
 
0.1%
-6300 36
 
0.1%
-8500 48
 
0.1%
-10600 42
 
0.1%
-12100 29
 
0.1%
-13200 38
 
0.1%
-13800 48
 
0.1%
-15600 36
 
0.1%
-15700 76
 
0.2%

Garage_Location
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False
39758 
True
 
242
ValueCountFrequency (%)
False 39758
99.4%
True 242
 
0.6%
2025-07-25T19:22:08.458584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2024-01-01 00:00:00
Maximum2024-03-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T19:22:08.725554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:08.834544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Accident_Type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
16817 
2
16110 
3
3710 
1
3363 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 16817
42.0%
2 16110
40.3%
3 3710
 
9.3%
1 3363
 
8.4%

Length

2025-07-25T19:22:08.912819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:08.966645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 16817
42.0%
2 16110
40.3%
3 3710
 
9.3%
1 3363
 
8.4%

Most occurring characters

ValueCountFrequency (%)
0 16817
42.0%
2 16110
40.3%
3 3710
 
9.3%
1 3363
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16817
42.0%
2 16110
40.3%
3 3710
 
9.3%
1 3363
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16817
42.0%
2 16110
40.3%
3 3710
 
9.3%
1 3363
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16817
42.0%
2 16110
40.3%
3 3710
 
9.3%
1 3363
 
8.4%

Collision_Type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
1
11728 
2
11047 
0
10152 
3
7073 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 11728
29.3%
2 11047
27.6%
0 10152
25.4%
3 7073
17.7%

Length

2025-07-25T19:22:09.038264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:09.089360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 11728
29.3%
2 11047
27.6%
0 10152
25.4%
3 7073
17.7%

Most occurring characters

ValueCountFrequency (%)
1 11728
29.3%
2 11047
27.6%
0 10152
25.4%
3 7073
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 11728
29.3%
2 11047
27.6%
0 10152
25.4%
3 7073
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 11728
29.3%
2 11047
27.6%
0 10152
25.4%
3 7073
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 11728
29.3%
2 11047
27.6%
0 10152
25.4%
3 7073
17.7%

Accident_Severity
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
1
14222 
2
11187 
0
11052 
3
3539 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 14222
35.6%
2 11187
28.0%
0 11052
27.6%
3 3539
 
8.8%

Length

2025-07-25T19:22:09.156483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:09.203026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 14222
35.6%
2 11187
28.0%
0 11052
27.6%
3 3539
 
8.8%

Most occurring characters

ValueCountFrequency (%)
1 14222
35.6%
2 11187
28.0%
0 11052
27.6%
3 3539
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 14222
35.6%
2 11187
28.0%
0 11052
27.6%
3 3539
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 14222
35.6%
2 11187
28.0%
0 11052
27.6%
3 3539
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 14222
35.6%
2 11187
28.0%
0 11052
27.6%
3 3539
 
8.8%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
3
11491 
1
8801 
2
8102 
0
8006 
4
3600 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row3
4th row0
5th row2

Common Values

ValueCountFrequency (%)
3 11491
28.7%
1 8801
22.0%
2 8102
20.3%
0 8006
20.0%
4 3600
 
9.0%

Length

2025-07-25T19:22:09.265103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:09.319595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 11491
28.7%
1 8801
22.0%
2 8102
20.3%
0 8006
20.0%
4 3600
 
9.0%

Most occurring characters

ValueCountFrequency (%)
3 11491
28.7%
1 8801
22.0%
2 8102
20.3%
0 8006
20.0%
4 3600
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 11491
28.7%
1 8801
22.0%
2 8102
20.3%
0 8006
20.0%
4 3600
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 11491
28.7%
1 8801
22.0%
2 8102
20.3%
0 8006
20.0%
4 3600
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 11491
28.7%
1 8801
22.0%
2 8102
20.3%
0 8006
20.0%
4 3600
 
9.0%

Acccident_State
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
NY
10493 
SC
9913 
WV
8736 
VA
4382 
NC
4375 
Other values (2)
2101 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters80000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVA
2nd rowNC
3rd rowPA
4th rowSC
5th rowOH

Common Values

ValueCountFrequency (%)
NY 10493
26.2%
SC 9913
24.8%
WV 8736
21.8%
VA 4382
11.0%
NC 4375
10.9%
PA 1207
 
3.0%
OH 894
 
2.2%

Length

2025-07-25T19:22:09.391394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:09.453119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ny 10493
26.2%
sc 9913
24.8%
wv 8736
21.8%
va 4382
11.0%
nc 4375
10.9%
pa 1207
 
3.0%
oh 894
 
2.2%

Most occurring characters

ValueCountFrequency (%)
N 14868
18.6%
C 14288
17.9%
V 13118
16.4%
Y 10493
13.1%
S 9913
12.4%
W 8736
10.9%
A 5589
 
7.0%
P 1207
 
1.5%
O 894
 
1.1%
H 894
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 14868
18.6%
C 14288
17.9%
V 13118
16.4%
Y 10493
13.1%
S 9913
12.4%
W 8736
10.9%
A 5589
 
7.0%
P 1207
 
1.5%
O 894
 
1.1%
H 894
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 14868
18.6%
C 14288
17.9%
V 13118
16.4%
Y 10493
13.1%
S 9913
12.4%
W 8736
10.9%
A 5589
 
7.0%
P 1207
 
1.5%
O 894
 
1.1%
H 894
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 14868
18.6%
C 14288
17.9%
V 13118
16.4%
Y 10493
13.1%
S 9913
12.4%
W 8736
10.9%
A 5589
 
7.0%
P 1207
 
1.5%
O 894
 
1.1%
H 894
 
1.1%

Acccident_City
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Springfield
6343 
Columbus
5953 
Northbend
5928 
Arlington
5767 
Riverwood
5556 
Other values (2)
10453 

Length

Max length11
Median length9
Mean length9.291
Min length8

Characters and Unicode

Total characters371640
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColumbus
2nd rowArlington
3rd rowNorthbend
4th rowColumbus
5th rowHillsdale

Common Values

ValueCountFrequency (%)
Springfield 6343
15.9%
Columbus 5953
14.9%
Northbend 5928
14.8%
Arlington 5767
14.4%
Riverwood 5556
13.9%
Hillsdale 5546
13.9%
Northbrook 4907
12.3%

Length

2025-07-25T19:22:09.544148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:09.605723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
springfield 6343
15.9%
columbus 5953
14.9%
northbend 5928
14.8%
arlington 5767
14.4%
riverwood 5556
13.9%
hillsdale 5546
13.9%
northbrook 4907
12.3%

Most occurring characters

ValueCountFrequency (%)
o 43481
 
11.7%
l 34701
 
9.3%
r 33408
 
9.0%
i 29555
 
8.0%
n 23805
 
6.4%
d 23373
 
6.3%
e 23373
 
6.3%
b 16788
 
4.5%
t 16602
 
4.5%
g 12110
 
3.3%
Other values (16) 114444
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 371640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 43481
 
11.7%
l 34701
 
9.3%
r 33408
 
9.0%
i 29555
 
8.0%
n 23805
 
6.4%
d 23373
 
6.3%
e 23373
 
6.3%
b 16788
 
4.5%
t 16602
 
4.5%
g 12110
 
3.3%
Other values (16) 114444
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 371640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 43481
 
11.7%
l 34701
 
9.3%
r 33408
 
9.0%
i 29555
 
8.0%
n 23805
 
6.4%
d 23373
 
6.3%
e 23373
 
6.3%
b 16788
 
4.5%
t 16602
 
4.5%
g 12110
 
3.3%
Other values (16) 114444
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 371640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 43481
 
11.7%
l 34701
 
9.3%
r 33408
 
9.0%
i 29555
 
8.0%
n 23805
 
6.4%
d 23373
 
6.3%
e 23373
 
6.3%
b 16788
 
4.5%
t 16602
 
4.5%
g 12110
 
3.3%
Other values (16) 114444
30.8%
Distinct1000
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:09.873516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length20
Mean length14.766975
Min length11

Characters and Unicode

Total characters590679
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7819 Oak St
2nd row7609 Rock St
3rd row4618 Flute Ave
4th row1229 5th Ave
5th row1643 Washington Hwy
ValueCountFrequency (%)
drive 6986
 
5.8%
lane 6872
 
5.7%
ridge 6853
 
5.7%
st 6796
 
5.7%
ave 6372
 
5.3%
hwy 6121
 
5.1%
4th 2274
 
1.9%
5th 2010
 
1.7%
texas 1905
 
1.6%
britain 1815
 
1.5%
Other values (961) 71996
60.0%
2025-07-25T19:22:10.218303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80000
 
13.5%
e 49691
 
8.4%
i 25334
 
4.3%
a 24183
 
4.1%
n 20843
 
3.5%
r 19666
 
3.3%
t 18943
 
3.2%
5 18713
 
3.2%
1 17834
 
3.0%
3 17518
 
3.0%
Other values (39) 297954
50.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 590679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
80000
 
13.5%
e 49691
 
8.4%
i 25334
 
4.3%
a 24183
 
4.1%
n 20843
 
3.5%
r 19666
 
3.3%
t 18943
 
3.2%
5 18713
 
3.2%
1 17834
 
3.0%
3 17518
 
3.0%
Other values (39) 297954
50.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 590679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
80000
 
13.5%
e 49691
 
8.4%
i 25334
 
4.3%
a 24183
 
4.1%
n 20843
 
3.5%
r 19666
 
3.3%
t 18943
 
3.2%
5 18713
 
3.2%
1 17834
 
3.0%
3 17518
 
3.0%
Other values (39) 297954
50.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 590679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
80000
 
13.5%
e 49691
 
8.4%
i 25334
 
4.3%
a 24183
 
4.1%
n 20843
 
3.5%
r 19666
 
3.3%
t 18943
 
3.2%
5 18713
 
3.2%
1 17834
 
3.0%
3 17518
 
3.0%
Other values (39) 297954
50.4%

Accident_Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.6352
Minimum0
Maximum23
Zeros2044
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:10.285929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.9515127
Coefficient of variation (CV)0.59745537
Kurtosis-1.1989171
Mean11.6352
Median Absolute Deviation (MAD)6
Skewness-0.034852126
Sum465408
Variance48.323529
MonotonicityNot monotonic
2025-07-25T19:22:10.353497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 2228
 
5.6%
17 2164
 
5.4%
0 2044
 
5.1%
23 2007
 
5.0%
16 1944
 
4.9%
10 1863
 
4.7%
4 1861
 
4.7%
13 1837
 
4.6%
6 1761
 
4.4%
9 1680
 
4.2%
Other values (14) 20611
51.5%
ValueCountFrequency (%)
0 2044
5.1%
1 1175
2.9%
2 1238
3.1%
3 2228
5.6%
4 1861
4.7%
5 1290
3.2%
6 1761
4.4%
7 1554
3.9%
8 1409
3.5%
9 1680
4.2%
ValueCountFrequency (%)
23 2007
5.0%
22 1532
3.8%
21 1664
4.2%
20 1407
3.5%
19 1587
4.0%
18 1669
4.2%
17 2164
5.4%
16 1944
4.9%
15 1591
4.0%
14 1611
4.0%

Num_of_Vehicles_Involved
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
1
23183 
3
14314 
2
 
1253
4
 
1250

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 23183
58.0%
3 14314
35.8%
2 1253
 
3.1%
4 1250
 
3.1%

Length

2025-07-25T19:22:10.423468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:10.469486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23183
58.0%
3 14314
35.8%
2 1253
 
3.1%
4 1250
 
3.1%

Most occurring characters

ValueCountFrequency (%)
1 23183
58.0%
3 14314
35.8%
2 1253
 
3.1%
4 1250
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23183
58.0%
3 14314
35.8%
2 1253
 
3.1%
4 1250
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23183
58.0%
3 14314
35.8%
2 1253
 
3.1%
4 1250
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23183
58.0%
3 14314
35.8%
2 1253
 
3.1%
4 1250
 
3.1%

Property_Damage
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
1
14477 
0
13482 
2
12041 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 14477
36.2%
0 13482
33.7%
2 12041
30.1%

Length

2025-07-25T19:22:10.539072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:10.581640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 14477
36.2%
0 13482
33.7%
2 12041
30.1%

Most occurring characters

ValueCountFrequency (%)
1 14477
36.2%
0 13482
33.7%
2 12041
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 14477
36.2%
0 13482
33.7%
2 12041
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 14477
36.2%
0 13482
33.7%
2 12041
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 14477
36.2%
0 13482
33.7%
2 12041
30.1%

Bodily_Injuries
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
2
13381 
0
13312 
1
13307 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
2 13381
33.5%
0 13312
33.3%
1 13307
33.3%

Length

2025-07-25T19:22:10.638898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:10.687944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 13381
33.5%
0 13312
33.3%
1 13307
33.3%

Most occurring characters

ValueCountFrequency (%)
2 13381
33.5%
0 13312
33.3%
1 13307
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 13381
33.5%
0 13312
33.3%
1 13307
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 13381
33.5%
0 13312
33.3%
1 13307
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 13381
33.5%
0 13312
33.3%
1 13307
33.3%

Witnesses
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
1
10231 
0
10108 
2
9907 
3
9754 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 10231
25.6%
0 10108
25.3%
2 9907
24.8%
3 9754
24.4%

Length

2025-07-25T19:22:10.742038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:10.797791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10231
25.6%
0 10108
25.3%
2 9907
24.8%
3 9754
24.4%

Most occurring characters

ValueCountFrequency (%)
1 10231
25.6%
0 10108
25.3%
2 9907
24.8%
3 9754
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10231
25.6%
0 10108
25.3%
2 9907
24.8%
3 9754
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10231
25.6%
0 10108
25.3%
2 9907
24.8%
3 9754
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10231
25.6%
0 10108
25.3%
2 9907
24.8%
3 9754
24.4%

Police_Report
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
2
13716 
0
13634 
1
12650 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 13716
34.3%
0 13634
34.1%
1 12650
31.6%

Length

2025-07-25T19:22:10.862689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:10.912083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 13716
34.3%
0 13634
34.1%
1 12650
31.6%

Most occurring characters

ValueCountFrequency (%)
2 13716
34.3%
0 13634
34.1%
1 12650
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 13716
34.3%
0 13634
34.1%
1 12650
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 13716
34.3%
0 13634
34.1%
1 12650
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 13716
34.3%
0 13634
34.1%
1 12650
31.6%
Distinct365
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2024-01-02 00:00:00
Maximum2026-05-09 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T19:22:10.980171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:11.090036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2024-01-01 00:00:00
Maximum2024-03-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T19:22:11.189198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:11.278023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Auto_Make
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5581
Minimum0
Maximum13
Zeros2713
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:11.340137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q310
95-th percentile13
Maximum13
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.015075
Coefficient of variation (CV)0.61223144
Kurtosis-1.2371413
Mean6.5581
Median Absolute Deviation (MAD)3
Skewness-0.023177176
Sum262324
Variance16.120827
MonotonicityNot monotonic
2025-07-25T19:22:11.400440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4 3255
 
8.1%
11 3247
 
8.1%
10 3118
 
7.8%
9 3108
 
7.8%
3 3085
 
7.7%
5 2889
 
7.2%
12 2782
 
7.0%
8 2742
 
6.9%
2 2736
 
6.8%
0 2713
 
6.8%
Other values (4) 10325
25.8%
ValueCountFrequency (%)
0 2713
6.8%
1 2701
6.8%
2 2736
6.8%
3 3085
7.7%
4 3255
8.1%
5 2889
7.2%
6 2266
5.7%
7 2668
6.7%
8 2742
6.9%
9 3108
7.8%
ValueCountFrequency (%)
13 2690
6.7%
12 2782
7.0%
11 3247
8.1%
10 3118
7.8%
9 3108
7.8%
8 2742
6.9%
7 2668
6.7%
6 2266
5.7%
5 2889
7.2%
4 3255
8.1%

Auto_Model
Real number (ℝ)

Zeros 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.339125
Minimum0
Maximum38
Zeros697
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:11.479021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median20
Q329
95-th percentile36
Maximum38
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.099968
Coefficient of variation (CV)0.57396436
Kurtosis-1.1913337
Mean19.339125
Median Absolute Deviation (MAD)9
Skewness-0.076487036
Sum773565
Variance123.2093
MonotonicityNot monotonic
2025-07-25T19:22:11.562160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
30 1751
 
4.4%
36 1663
 
4.2%
27 1504
 
3.8%
4 1489
 
3.7%
23 1443
 
3.6%
20 1399
 
3.5%
28 1291
 
3.2%
21 1286
 
3.2%
5 1212
 
3.0%
29 1188
 
3.0%
Other values (29) 25774
64.4%
ValueCountFrequency (%)
0 697
1.7%
1 1047
2.6%
2 1007
2.5%
3 1064
2.7%
4 1489
3.7%
5 1212
3.0%
6 541
 
1.4%
7 767
1.9%
8 836
2.1%
9 1088
2.7%
ValueCountFrequency (%)
38 625
 
1.6%
37 885
2.2%
36 1663
4.2%
35 954
2.4%
34 987
2.5%
33 828
2.1%
32 947
2.4%
31 442
 
1.1%
30 1751
4.4%
29 1188
3.0%

Auto_Year
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5139
Minimum0
Maximum9
Zeros4326
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:11.634152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8499403
Coefficient of variation (CV)0.63136984
Kurtosis-1.2317176
Mean4.5139
Median Absolute Deviation (MAD)2
Skewness-0.079136501
Sum180556
Variance8.1221598
MonotonicityNot monotonic
2025-07-25T19:22:11.695249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 5792
14.5%
0 4326
10.8%
6 4019
10.0%
3 3970
9.9%
5 3816
9.5%
2 3816
9.5%
1 3679
9.2%
8 3621
9.1%
4 3604
9.0%
9 3357
8.4%
ValueCountFrequency (%)
0 4326
10.8%
1 3679
9.2%
2 3816
9.5%
3 3970
9.9%
4 3604
9.0%
5 3816
9.5%
6 4019
10.0%
7 5792
14.5%
8 3621
9.1%
9 3357
8.4%
ValueCountFrequency (%)
9 3357
8.4%
8 3621
9.1%
7 5792
14.5%
6 4019
10.0%
5 3816
9.5%
4 3604
9.0%
3 3970
9.9%
2 3816
9.5%
1 3679
9.2%
0 4326
10.8%

Vehicle_Color
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.493875
Minimum0
Maximum5
Zeros9173
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:11.750817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9090059
Coefficient of variation (CV)0.76547779
Kurtosis-1.4776308
Mean2.493875
Median Absolute Deviation (MAD)2
Skewness0.060153506
Sum99755
Variance3.6443036
MonotonicityNot monotonic
2025-07-25T19:22:11.806474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 10261
25.7%
0 9173
22.9%
2 7357
18.4%
1 5158
12.9%
4 4425
11.1%
3 3626
 
9.1%
ValueCountFrequency (%)
0 9173
22.9%
1 5158
12.9%
2 7357
18.4%
3 3626
 
9.1%
4 4425
11.1%
5 10261
25.7%
ValueCountFrequency (%)
5 10261
25.7%
4 4425
11.1%
3 3626
 
9.1%
2 7357
18.4%
1 5158
12.9%
0 9173
22.9%

Vehicle_Cost
Real number (ℝ)

High correlation  Uniform 

Distinct39468
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19665.996
Minimum0
Maximum39467
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:11.892913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1963.95
Q19785.75
median19637.5
Q329520.25
95-th percentile37468.05
Maximum39467
Range39467
Interquartile range (IQR)19734.5

Descriptive statistics

Standard deviation11391.376
Coefficient of variation (CV)0.57924226
Kurtosis-1.2001035
Mean19665.996
Median Absolute Deviation (MAD)9867.5
Skewness0.0064016661
Sum7.8663985 × 108
Variance1.2976345 × 108
MonotonicityNot monotonic
2025-07-25T19:22:11.992071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10021 3
 
< 0.1%
15502 3
 
< 0.1%
8286 3
 
< 0.1%
1554 3
 
< 0.1%
30218 3
 
< 0.1%
20773 3
 
< 0.1%
15255 3
 
< 0.1%
1207 3
 
< 0.1%
2525 3
 
< 0.1%
19495 3
 
< 0.1%
Other values (39458) 39970
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
39467 1
< 0.1%
39466 1
< 0.1%
39465 1
< 0.1%
39464 1
< 0.1%
39463 1
< 0.1%
39462 1
< 0.1%
39461 1
< 0.1%
39460 1
< 0.1%
39459 1
< 0.1%
39458 1
< 0.1%

Annual_Mileage
Real number (ℝ)

High correlation 

Distinct12420
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11514.875
Minimum5000
Maximum18000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:12.099227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile5651
Q18269
median11525
Q314744.25
95-th percentile17381
Maximum18000
Range13000
Interquartile range (IQR)6475.25

Descriptive statistics

Standard deviation3751.5059
Coefficient of variation (CV)0.32579649
Kurtosis-1.1939645
Mean11514.875
Median Absolute Deviation (MAD)3237
Skewness-0.0036930663
Sum4.6059501 × 108
Variance14073796
MonotonicityNot monotonic
2025-07-25T19:22:12.196917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7478 12
 
< 0.1%
8149 11
 
< 0.1%
14053 11
 
< 0.1%
5671 11
 
< 0.1%
8365 11
 
< 0.1%
15058 11
 
< 0.1%
5258 10
 
< 0.1%
9412 10
 
< 0.1%
7054 10
 
< 0.1%
12308 10
 
< 0.1%
Other values (12410) 39893
99.7%
ValueCountFrequency (%)
5000 2
 
< 0.1%
5001 5
< 0.1%
5003 2
 
< 0.1%
5004 2
 
< 0.1%
5005 3
< 0.1%
5006 4
< 0.1%
5007 4
< 0.1%
5008 3
< 0.1%
5009 6
< 0.1%
5010 2
 
< 0.1%
ValueCountFrequency (%)
18000 3
 
< 0.1%
17999 2
 
< 0.1%
17998 2
 
< 0.1%
17997 4
< 0.1%
17996 2
 
< 0.1%
17995 1
 
< 0.1%
17994 6
< 0.1%
17993 9
< 0.1%
17992 2
 
< 0.1%
17991 2
 
< 0.1%

DiffIN_Mileage
Real number (ℝ)

Distinct5991
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4991.5305
Minimum2000
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:12.295005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2300
Q13476
median4998
Q36479.25
95-th percentile7693
Maximum8000
Range6000
Interquartile range (IQR)3003.25

Descriptive statistics

Standard deviation1733.428
Coefficient of variation (CV)0.34727385
Kurtosis-1.2042321
Mean4991.5305
Median Absolute Deviation (MAD)1502
Skewness0.0015414506
Sum1.9966122 × 108
Variance3004772.7
MonotonicityNot monotonic
2025-07-25T19:22:12.621791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2353 16
 
< 0.1%
6979 16
 
< 0.1%
2938 16
 
< 0.1%
6073 16
 
< 0.1%
5026 16
 
< 0.1%
6180 16
 
< 0.1%
2891 16
 
< 0.1%
4977 16
 
< 0.1%
5764 16
 
< 0.1%
5650 15
 
< 0.1%
Other values (5981) 39841
99.6%
ValueCountFrequency (%)
2000 9
< 0.1%
2001 8
< 0.1%
2002 3
 
< 0.1%
2003 4
 
< 0.1%
2004 10
< 0.1%
2005 3
 
< 0.1%
2006 7
< 0.1%
2007 10
< 0.1%
2008 5
< 0.1%
2009 7
< 0.1%
ValueCountFrequency (%)
8000 8
< 0.1%
7999 8
< 0.1%
7998 9
< 0.1%
7997 6
< 0.1%
7996 2
 
< 0.1%
7995 9
< 0.1%
7994 4
< 0.1%
7993 6
< 0.1%
7992 6
< 0.1%
7991 5
< 0.1%

Low_Mileage_Discount
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
32376 
1
7624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 32376
80.9%
1 7624
 
19.1%

Length

2025-07-25T19:22:12.702439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:12.741997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 32376
80.9%
1 7624
 
19.1%

Most occurring characters

ValueCountFrequency (%)
0 32376
80.9%
1 7624
 
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32376
80.9%
1 7624
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32376
80.9%
1 7624
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32376
80.9%
1 7624
 
19.1%

Fraud_Ind
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
30120 
1
9880 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 30120
75.3%
1 9880
 
24.7%

Length

2025-07-25T19:22:12.799721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:12.843787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 30120
75.3%
1 9880
 
24.7%

Most occurring characters

ValueCountFrequency (%)
0 30120
75.3%
1 9880
 
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 30120
75.3%
1 9880
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 30120
75.3%
1 9880
 
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 30120
75.3%
1 9880
 
24.7%

Commute_Discount
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0
39217 
1
 
783

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39217
98.0%
1 783
 
2.0%

Length

2025-07-25T19:22:12.907989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:12.947033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 39217
98.0%
1 783
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 39217
98.0%
1 783
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 39217
98.0%
1 783
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 39217
98.0%
1 783
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 39217
98.0%
1 783
 
2.0%

Total_Claim
Real number (ℝ)

High correlation 

Distinct39692
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13951.283
Minimum500.5
Maximum124800.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:13.010660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500.5
5-th percentile1750.3205
Q16738.325
median12978.85
Q319272.135
95-th percentile24245.684
Maximum124800.15
Range124299.65
Interquartile range (IQR)12533.81

Descriptive statistics

Standard deviation11716.935
Coefficient of variation (CV)0.83984639
Kurtosis30.522216
Mean13951.283
Median Absolute Deviation (MAD)6265.445
Skewness4.329865
Sum5.5805134 × 108
Variance1.3728657 × 108
MonotonicityNot monotonic
2025-07-25T19:22:13.110258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1595.41 2
 
< 0.1%
6811.39 2
 
< 0.1%
12880.53 2
 
< 0.1%
21334.81 2
 
< 0.1%
5432.89 2
 
< 0.1%
4934.48 2
 
< 0.1%
14749.86 2
 
< 0.1%
13941.31 2
 
< 0.1%
19203.57 2
 
< 0.1%
23606.3 2
 
< 0.1%
Other values (39682) 39980
> 99.9%
ValueCountFrequency (%)
500.5 1
< 0.1%
500.78 1
< 0.1%
501.19 1
< 0.1%
501.61 1
< 0.1%
503.42 1
< 0.1%
503.63 1
< 0.1%
504.56 1
< 0.1%
504.81 1
< 0.1%
505.46 1
< 0.1%
505.5 1
< 0.1%
ValueCountFrequency (%)
124800.15 1
< 0.1%
124792.9 1
< 0.1%
124656.66 1
< 0.1%
124544.09 1
< 0.1%
124336.07 1
< 0.1%
124326.95 1
< 0.1%
124323.38 1
< 0.1%
124150.33 1
< 0.1%
123870.15 1
< 0.1%
123799 1
< 0.1%

Injury_Claim
Real number (ℝ)

High correlation 

Distinct39044
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4624.4331
Minimum0.1
Maximum110881.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:13.211329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile165.9595
Q11069.505
median2935.56
Q36432.6575
95-th percentile13831.655
Maximum110881.29
Range110881.19
Interquartile range (IQR)5363.1525

Descriptive statistics

Standard deviation5691.8822
Coefficient of variation (CV)1.2308281
Kurtosis44.933067
Mean4624.4331
Median Absolute Deviation (MAD)2239.47
Skewness4.7503209
Sum1.8497732 × 108
Variance32397523
MonotonicityNot monotonic
2025-07-25T19:22:13.310465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.93 3
 
< 0.1%
934.78 3
 
< 0.1%
607.21 3
 
< 0.1%
653.69 3
 
< 0.1%
698.41 3
 
< 0.1%
714.06 3
 
< 0.1%
761.59 3
 
< 0.1%
4952.84 3
 
< 0.1%
936.29 3
 
< 0.1%
1997.14 3
 
< 0.1%
Other values (39034) 39970
99.9%
ValueCountFrequency (%)
0.1 1
< 0.1%
0.16 1
< 0.1%
0.22 1
< 0.1%
0.28 1
< 0.1%
0.3 1
< 0.1%
0.32 1
< 0.1%
0.37 1
< 0.1%
0.42 1
< 0.1%
0.46 1
< 0.1%
0.52 1
< 0.1%
ValueCountFrequency (%)
110881.29 1
< 0.1%
104428.15 1
< 0.1%
98980.65 1
< 0.1%
91310 1
< 0.1%
88903.77 1
< 0.1%
88787.5 1
< 0.1%
88002.34 1
< 0.1%
86677.63 1
< 0.1%
82919.69 1
< 0.1%
82626.69 1
< 0.1%

Property_Claim
Real number (ℝ)

High correlation 

Distinct39024
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4668.0915
Minimum0.12
Maximum109176.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:13.392863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile163.315
Q11058.97
median2946.64
Q36486.4575
95-th percentile13975.612
Maximum109176.92
Range109176.8
Interquartile range (IQR)5427.4875

Descriptive statistics

Standard deviation5798.0082
Coefficient of variation (CV)1.2420511
Kurtosis45.953359
Mean4668.0915
Median Absolute Deviation (MAD)2264.54
Skewness4.8200446
Sum1.8672366 × 108
Variance33616900
MonotonicityNot monotonic
2025-07-25T19:22:13.484093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
423.12 3
 
< 0.1%
90.2 3
 
< 0.1%
334 3
 
< 0.1%
1004.73 3
 
< 0.1%
257.14 3
 
< 0.1%
225.19 3
 
< 0.1%
590.29 3
 
< 0.1%
12.13 3
 
< 0.1%
486.51 3
 
< 0.1%
672.63 3
 
< 0.1%
Other values (39014) 39970
99.9%
ValueCountFrequency (%)
0.12 1
< 0.1%
0.23 2
< 0.1%
0.28 1
< 0.1%
0.41 1
< 0.1%
0.48 1
< 0.1%
0.49 1
< 0.1%
0.5 1
< 0.1%
0.55 1
< 0.1%
0.6 1
< 0.1%
0.66 1
< 0.1%
ValueCountFrequency (%)
109176.92 1
< 0.1%
103039.43 1
< 0.1%
95370.83 1
< 0.1%
94543.71 1
< 0.1%
92796.26 1
< 0.1%
92578.86 1
< 0.1%
91395.72 1
< 0.1%
91220.28 1
< 0.1%
90015.93 1
< 0.1%
89837.08 1
< 0.1%

Vehicle_Claim
Real number (ℝ)

High correlation 

Distinct39027
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4658.7589
Minimum0.05
Maximum123443.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:13.573813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile170.129
Q11069.015
median2931.14
Q36434.28
95-th percentile13903.03
Maximum123443.92
Range123443.87
Interquartile range (IQR)5365.265

Descriptive statistics

Standard deviation5955.1534
Coefficient of variation (CV)1.2782704
Kurtosis59.597341
Mean4658.7589
Median Absolute Deviation (MAD)2240.2
Skewness5.5330453
Sum1.8635036 × 108
Variance35463852
MonotonicityNot monotonic
2025-07-25T19:22:13.669453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.65 4
 
< 0.1%
55.58 3
 
< 0.1%
576.07 3
 
< 0.1%
758.45 3
 
< 0.1%
100.72 3
 
< 0.1%
2179.68 3
 
< 0.1%
355.29 3
 
< 0.1%
901.53 3
 
< 0.1%
243.78 3
 
< 0.1%
814.35 3
 
< 0.1%
Other values (39017) 39969
99.9%
ValueCountFrequency (%)
0.05 1
< 0.1%
0.12 1
< 0.1%
0.29 1
< 0.1%
0.31 1
< 0.1%
0.34 1
< 0.1%
0.35 1
< 0.1%
0.38 1
< 0.1%
0.47 1
< 0.1%
0.8 1
< 0.1%
0.89 1
< 0.1%
ValueCountFrequency (%)
123443.92 1
< 0.1%
120248.01 1
< 0.1%
110793.62 1
< 0.1%
104118.42 1
< 0.1%
102161.66 1
< 0.1%
99565.62 1
< 0.1%
97752.2 1
< 0.1%
97141.8 1
< 0.1%
95707.62 1
< 0.1%
95228.96 1
< 0.1%
Distinct40000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:13.869938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters360000
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40000 ?
Unique (%)100.0%

Sample

1st rowIF47V1395
2nd rowEI51L7783
3rd rowMU37B8905
4th rowRI52Q2108
5th rowUX39O9355
ValueCountFrequency (%)
bo39h8755 1
 
< 0.1%
lw96p6191 1
 
< 0.1%
rh78o6052 1
 
< 0.1%
wl64r4628 1
 
< 0.1%
al10u7726 1
 
< 0.1%
gl82i4112 1
 
< 0.1%
ca53e2408 1
 
< 0.1%
ig87m9245 1
 
< 0.1%
pf04a5492 1
 
< 0.1%
uc61e1433 1
 
< 0.1%
Other values (39990) 39990
> 99.9%
2025-07-25T19:22:14.113803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 24177
 
6.7%
7 24149
 
6.7%
0 24137
 
6.7%
5 24068
 
6.7%
6 24040
 
6.7%
9 24021
 
6.7%
1 23938
 
6.6%
4 23932
 
6.6%
8 23908
 
6.6%
3 23630
 
6.6%
Other values (26) 120000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 24177
 
6.7%
7 24149
 
6.7%
0 24137
 
6.7%
5 24068
 
6.7%
6 24040
 
6.7%
9 24021
 
6.7%
1 23938
 
6.6%
4 23932
 
6.6%
8 23908
 
6.6%
3 23630
 
6.6%
Other values (26) 120000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 24177
 
6.7%
7 24149
 
6.7%
0 24137
 
6.7%
5 24068
 
6.7%
6 24040
 
6.7%
9 24021
 
6.7%
1 23938
 
6.6%
4 23932
 
6.6%
8 23908
 
6.6%
3 23630
 
6.6%
Other values (26) 120000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 24177
 
6.7%
7 24149
 
6.7%
0 24137
 
6.7%
5 24068
 
6.7%
6 24040
 
6.7%
9 24021
 
6.7%
1 23938
 
6.6%
4 23932
 
6.6%
8 23908
 
6.6%
3 23630
 
6.6%
Other values (26) 120000
33.3%

Check_Point
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False
40000 
ValueCountFrequency (%)
False 40000
100.0%
2025-07-25T19:22:14.162350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Wait_Policy_BI
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
250
14047 
100
13962 
500
11991 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row500
2nd row250
3rd row500
4th row500
5th row250

Common Values

ValueCountFrequency (%)
250 14047
35.1%
100 13962
34.9%
500 11991
30.0%

Length

2025-07-25T19:22:14.214916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:14.261976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
250 14047
35.1%
100 13962
34.9%
500 11991
30.0%

Most occurring characters

ValueCountFrequency (%)
0 65953
55.0%
5 26038
 
21.7%
2 14047
 
11.7%
1 13962
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 65953
55.0%
5 26038
 
21.7%
2 14047
 
11.7%
1 13962
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 65953
55.0%
5 26038
 
21.7%
2 14047
 
11.7%
1 13962
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 65953
55.0%
5 26038
 
21.7%
2 14047
 
11.7%
1 13962
 
11.6%

Indemnity_Policy_BI
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
500
14047 
300
13962 
1000
11991 

Length

Max length4
Median length3
Mean length3.299775
Min length3

Characters and Unicode

Total characters131991
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row500
3rd row1000
4th row1000
5th row500

Common Values

ValueCountFrequency (%)
500 14047
35.1%
300 13962
34.9%
1000 11991
30.0%

Length

2025-07-25T19:22:14.324281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:14.371848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
500 14047
35.1%
300 13962
34.9%
1000 11991
30.0%

Most occurring characters

ValueCountFrequency (%)
0 91991
69.7%
5 14047
 
10.6%
3 13962
 
10.6%
1 11991
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 91991
69.7%
5 14047
 
10.6%
3 13962
 
10.6%
1 11991
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 91991
69.7%
5 14047
 
10.6%
3 13962
 
10.6%
1 11991
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 91991
69.7%
5 14047
 
10.6%
3 13962
 
10.6%
1 11991
 
9.1%

Policy_Duration
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
182
19183 
183
15873 
184
4944 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row183
2nd row183
3rd row182
4th row184
5th row182

Common Values

ValueCountFrequency (%)
182 19183
48.0%
183 15873
39.7%
184 4944
 
12.4%

Length

2025-07-25T19:22:14.433092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T19:22:14.482109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
182 19183
48.0%
183 15873
39.7%
184 4944
 
12.4%

Most occurring characters

ValueCountFrequency (%)
1 40000
33.3%
8 40000
33.3%
2 19183
16.0%
3 15873
 
13.2%
4 4944
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 40000
33.3%
8 40000
33.3%
2 19183
16.0%
3 15873
 
13.2%
4 4944
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 40000
33.3%
8 40000
33.3%
2 19183
16.0%
3 15873
 
13.2%
4 4944
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 40000
33.3%
8 40000
33.3%
2 19183
16.0%
3 15873
 
13.2%
4 4944
 
4.1%

Claim_Duration
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.57595
Minimum0
Maximum24
Zeros6271
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:14.544175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9217502
Coefficient of variation (CV)0.74603553
Kurtosis24.952476
Mean2.57595
Median Absolute Deviation (MAD)1
Skewness2.4278849
Sum103038
Variance3.6931239
MonotonicityNot monotonic
2025-07-25T19:22:14.599357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 7387
18.5%
4 6848
17.1%
1 6581
16.5%
5 6450
16.1%
2 6381
16.0%
0 6271
15.7%
24 44
 
0.1%
22 38
 
0.1%
ValueCountFrequency (%)
0 6271
15.7%
1 6581
16.5%
2 6381
16.0%
3 7387
18.5%
4 6848
17.1%
5 6450
16.1%
22 38
 
0.1%
24 44
 
0.1%
ValueCountFrequency (%)
24 44
 
0.1%
22 38
 
0.1%
5 6450
16.1%
4 6848
17.1%
3 7387
18.5%
2 6381
16.0%
1 6581
16.5%
0 6271
15.7%

License_Validity
Real number (ℝ)

Distinct338
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean615.45565
Minimum-31
Maximum800
Zeros0
Zeros (%)0.0%
Negative120
Negative (%)0.3%
Memory size312.6 KiB
2025-07-25T19:22:14.685600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-31
5-th percentile457
Q1525
median613
Q3708
95-th percentile784
Maximum800
Range831
Interquartile range (IQR)183

Descriptive statistics

Standard deviation110.31943
Coefficient of variation (CV)0.17924838
Kurtosis1.8530167
Mean615.45565
Median Absolute Deviation (MAD)90
Skewness-0.49379764
Sum24618226
Variance12170.376
MonotonicityNot monotonic
2025-07-25T19:22:14.767715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 384
 
1.0%
493 322
 
0.8%
653 300
 
0.8%
442 286
 
0.7%
464 285
 
0.7%
727 276
 
0.7%
468 271
 
0.7%
792 270
 
0.7%
564 258
 
0.6%
505 248
 
0.6%
Other values (328) 37100
92.8%
ValueCountFrequency (%)
-31 88
 
0.2%
-3 32
 
0.1%
440 120
0.3%
441 27
 
0.1%
442 286
0.7%
444 38
 
0.1%
445 45
 
0.1%
446 185
0.5%
447 196
0.5%
448 162
0.4%
ValueCountFrequency (%)
800 131
0.3%
799 150
0.4%
798 114
0.3%
797 89
 
0.2%
796 95
 
0.2%
795 29
 
0.1%
794 211
0.5%
793 40
 
0.1%
792 270
0.7%
791 72
 
0.2%

Claim_Intensity
Real number (ℝ)

High correlation  Unique 

Distinct40000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3080196
Minimum0.014704362
Maximum24.346494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.6 KiB
2025-07-25T19:22:14.859323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.014704362
5-th percentile0.12611242
Q10.48880897
median0.97620152
Q31.691503
95-th percentile3.481008
Maximum24.346494
Range24.33179
Interquartile range (IQR)1.202694

Descriptive statistics

Standard deviation1.3972289
Coefficient of variation (CV)1.0682018
Kurtosis49.191414
Mean1.3080196
Median Absolute Deviation (MAD)0.56118633
Skewness5.1224974
Sum52320.782
Variance1.9522485
MonotonicityNot monotonic
2025-07-25T19:22:14.952852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.08651982 1
 
< 0.1%
1.2549517 1
 
< 0.1%
1.587984703 1
 
< 0.1%
2.200506195 1
 
< 0.1%
0.3420427459 1
 
< 0.1%
1.028926965 1
 
< 0.1%
1.764629219 1
 
< 0.1%
0.6152593894 1
 
< 0.1%
0.6787233257 1
 
< 0.1%
4.930511476 1
 
< 0.1%
Other values (39990) 39990
> 99.9%
ValueCountFrequency (%)
0.01470436223 1
< 0.1%
0.01491654901 1
< 0.1%
0.01539820592 1
< 0.1%
0.01598206206 1
< 0.1%
0.01656775198 1
< 0.1%
0.01660708909 1
< 0.1%
0.01673108375 1
< 0.1%
0.01676805407 1
< 0.1%
0.01690968735 1
< 0.1%
0.01737441711 1
< 0.1%
ValueCountFrequency (%)
24.34649429 1
< 0.1%
23.60781233 1
< 0.1%
23.21718458 1
< 0.1%
22.34737802 1
< 0.1%
21.98347044 1
< 0.1%
21.47589948 1
< 0.1%
20.87335621 1
< 0.1%
20.62194693 1
< 0.1%
20.55833118 1
< 0.1%
20.47413589 1
< 0.1%

Interactions

2025-07-25T19:22:02.062313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.171813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.718155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.548230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.322721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.553211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.403687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.697373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.525831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.547428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.361628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.324320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.152301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.275506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.050112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.834994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.170903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.082543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.217930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.128306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.918996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.461743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.254806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.058163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.139403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.238297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.777619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.620650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.403531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.631603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.489337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.773495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.613090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.612000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.426956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.397611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.229794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.350632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.121957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.925288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.245548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.163632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.296102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.206127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.981169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.525141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.335109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.129265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.211937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.299293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.834176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.691372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.484566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.704102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.568864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.839072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.681309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.677120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.495557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.468214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.307905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.426772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.198101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.999883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.319305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.242740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.368464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.278407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.042782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.587329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.403265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.204869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.289453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.361336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.904825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.769524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.560769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.778345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.649501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.910201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.749279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.750275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.563669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.541848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.388068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.498174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.275752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:45.087132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.395107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.319006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.458678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.349517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.103564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.648383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.471018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.271472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.366487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.428297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.984556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.842620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.646886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.865117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.743108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.995356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.833965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.831729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.640557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.619650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.475797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.587973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.346159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:45.179917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.481933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.404704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.539136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.431435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.168187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.719985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.551625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.350589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.440577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.494894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.049122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.916693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.727394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.939924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.840656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.072707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.909044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.908974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.709791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.696619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.571815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.665496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.419517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:45.483714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.557250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.681049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.609420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.507032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.229881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.783864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.628056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.421910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.520202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.568393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.116494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.997323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.813494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.023134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.927598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.152648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.998980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.990041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.789158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.774236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.659470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.745805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.501838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:45.577371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.637242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.763700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.693180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.592335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.300529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.857502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.709326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.506012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.592406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.629316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.184316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.072957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.889691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.100175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.016407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.221864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.068741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.064686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.856636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.851383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.739090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.815637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.575468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:45.664029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.717804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.834524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.775085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.665421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.360702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.919633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.785880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.579386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.663878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.694787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.398932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.143696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.980163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.187792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.106385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.294487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.143073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.142877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.930754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.922586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.811339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.889771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.648733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:45.746267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.794958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.918375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.850783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.932130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.421568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.985683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.856472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.656518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.741627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.756320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.479038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.216931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.063576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.266986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.188009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.364571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.216877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.210523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.007831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.007015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.893584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:41.964888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.732451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:45.841101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.877674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.995965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.921721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.994555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.482137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.049515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.936571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.733615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.811280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.820287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.551166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.287619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.137218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.342016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.477729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.437112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.290012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.287221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.077496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.072105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.965760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.036113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.804191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:45.944937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.948864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.071863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.002027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.053827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.544095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.109718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.009287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.820943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.884386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.883211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.621614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.360293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.409049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.420551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.564883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.519380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.359824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.364428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.149195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.141406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.043962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.097181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.882082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.051875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.026146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.143508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.078676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.115880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.607165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.172773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.077406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.897381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:02.958566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:18.954391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.690746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.435109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.489656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.504219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.651496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.604985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.436829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.441910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.218636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.214997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.118878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.173790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:43.951231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.146881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.101225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.220591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.160563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.179520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.677177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.240633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.169058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:00.973529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.025165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.013401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.759057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.502223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.574766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.578462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.745661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.674639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.724965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.515380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.287904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.284023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.190125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.244874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.013790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.234541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.185040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.295870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.229645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.236170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.735152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.493511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.237488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.047627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.098338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.076353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.831174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.578821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.662470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.649322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.830722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.755486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.815350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.592485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.362528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.365630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.266100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.311996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.091461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.327857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.260047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.378585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.309251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.297170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.797214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.555086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.304603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.119482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.182707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.141445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.901921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.653421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.756358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.730651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:28.916846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.839899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.903291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.672900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.441223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.448574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.348101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.388394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.165071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.425761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.355926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.474432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.399709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.362233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.864298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.623178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.385517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.194654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.260450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.219387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:20.974042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.730149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.837502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.802293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.007698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.923689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:32.979829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.753046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.515482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.525159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.430206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.463812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.242290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.509853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.431253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.567210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.482698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.424464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:55.937590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.690531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.464834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.270276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.332716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.282490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.042675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.812222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:24.917804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.878903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.089719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:30.997328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.051475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.831247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.587993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.604386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.510942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.536877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.312424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.593713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.506366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.649383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.565197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.488243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.005619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.755625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.537225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.354149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.410326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.346101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.114896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.887766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.000723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:26.949119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.172417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.069645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.126717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.909752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.671559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.679780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.593865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.612122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.390826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.694015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.591708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.727276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.648305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.552253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.070400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.825351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.611564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.430012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.478480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.406485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.188513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:22.962598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.097878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.028525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.258854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.141226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.203271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:34.995104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.743827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.759832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.674533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.682571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.459862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.774632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.672803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.807427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.729896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.610509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.132239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.898493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.682109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.502466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.562440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.468579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.258868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.033688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.195590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.104335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.337283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.216772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.275757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.069922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:36.817469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.834922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.752656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.750670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.549846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.861248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.759234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.890522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.803929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.671585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.191294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:57.975600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.755732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.576169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.633776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.532043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.327182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.107785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.296583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.182939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.433891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.292473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.347708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.145245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.107463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.916697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.832042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.825475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.623050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:46.950374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.841388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:50.977161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.891791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.735214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.260041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.045163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.832765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.857434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.712642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.596078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.409351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.179621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.382226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.254385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.526758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.369716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.412947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.217391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.185896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:38.995185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.908652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.906768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.692696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.025511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:48.920953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.055246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:52.971662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.799457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.329503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.109300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.904343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.929784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:03.790022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:19.657480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:21.475320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:23.250939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:25.468349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:27.324467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:29.610974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:31.454754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:33.485722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:35.286514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:37.255222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:39.063808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:40.985775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:42.974908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:44.764877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:47.101095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:49.002618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:51.135520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:53.045905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:54.859670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:56.397799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:58.180509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:21:59.982248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T19:22:01.992231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-25T19:22:15.072760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Acccident_CityAcccident_StateAccident_HourAccident_SeverityAccident_TypeAge_InsuredAnnual_MileageAuto_MakeAuto_ModelAuto_YearBodily_InjuriesCapital_GainsCapital_LossClaim_DurationClaim_IntensityCollision_TypeCommute_DiscountCustomer_Life_Value1DiffIN_MileageEducationFraud_IndGarage_LocationHobbiesIndemnity_Policy_BIInjury_ClaimInsured_RelationshipInsured_ZipLicense_ValidityLow_Mileage_DiscountNum_of_Vehicles_InvolvedOccupationPolice_ReportPolicy_BIPolicy_DedPolicy_DurationPolicy_NumPolicy_PremiumPolicy_StateProperty_ClaimProperty_DamageTotal_ClaimUmbrella_LimitVehicle_ClaimVehicle_ColorVehicle_CostWait_Policy_BIWitnessesauthorities_contacted
Acccident_City1.0000.0600.0880.0630.0810.0910.0050.0840.0910.0900.0710.0830.0920.0730.0140.0800.0140.0000.0060.0720.0480.0730.1380.0620.0000.0760.0810.0870.0000.0750.1050.0710.0620.0640.0620.0850.0950.0820.0040.1030.0030.1110.0050.0320.0750.0620.0950.089
Acccident_State0.0601.0000.1020.0830.0800.0840.0000.1040.1130.1010.0780.0900.0920.0730.0150.0950.0070.0000.0000.0850.1290.0430.1520.0630.0130.0840.0830.0620.0000.0830.1210.0830.0630.0830.0550.1110.0950.0700.0000.0610.0000.1110.0000.0350.0790.0630.0800.078
Accident_Hour0.0880.1021.0000.2180.2820.1050.001-0.004-0.051-0.0690.106-0.013-0.0320.0350.0400.2910.012-0.0070.001-0.0630.1500.0640.1310.0790.0010.0970.0130.0380.0120.1500.1030.0860.0790.1280.0970.034-0.0000.102-0.0030.086-0.002-0.015-0.005-0.008-0.0750.0790.1100.199
Accident_Severity0.0630.0830.2181.0000.4260.1060.0000.0750.0900.0920.0280.0880.0870.0610.0130.4260.0370.0000.0150.0740.5150.0990.1370.0550.0070.0640.0660.0460.0040.1770.1140.0610.0550.0250.0650.0930.0760.0360.0090.0840.0040.1010.0000.0280.0880.0550.0590.314
Accident_Type0.0810.0800.2820.4261.0000.0890.0060.0810.1140.0780.0530.0950.0830.0490.0130.5790.0240.0000.0000.0790.1720.0790.1420.0560.0000.0590.0710.0490.0110.5770.1170.0590.0560.0610.1050.0960.0940.0430.0000.0350.0110.0690.0000.0130.0520.0560.0470.447
Age_Insured0.0910.0840.1050.1060.0891.0000.0050.0130.018-0.0140.082-0.021-0.0020.0170.0080.1140.0000.0010.004-0.0000.0730.1170.1300.0890.0010.098-0.0010.0350.0000.1210.1110.0730.0890.0870.0720.0530.0290.0800.0130.0880.010-0.0040.008-0.016-0.0020.0890.1040.091
Annual_Mileage0.0050.0000.0010.0000.0060.0051.000-0.009-0.001-0.0010.0090.0100.005-0.0000.0030.0070.009-0.006-0.0030.0050.0000.0140.0090.0090.0050.000-0.0030.0020.9780.0080.0000.0000.0090.0000.0030.005-0.0040.0000.0080.0000.0060.0140.0030.0020.0030.0090.0080.000
Auto_Make0.0840.104-0.0040.0750.0810.013-0.0091.000-0.186-0.0020.0570.040-0.045-0.0190.0480.0820.014-0.0030.0030.0520.0700.1200.1440.0790.0040.099-0.029-0.0130.0100.0830.1030.0860.0790.0900.118-0.0260.0100.1170.0010.0820.0010.0020.007-0.017-0.0820.0790.0830.094
Auto_Model0.0910.113-0.0510.0900.1140.018-0.001-0.1861.0000.0100.0580.040-0.0330.022-0.0740.0790.0060.0020.0030.0490.0930.1550.1480.088-0.0080.0960.034-0.0370.0110.1090.1100.0970.0880.0970.109-0.031-0.0300.102-0.0110.075-0.0110.023-0.0030.1180.1140.0880.0790.093
Auto_Year0.0900.101-0.0690.0920.078-0.014-0.001-0.0020.0101.0000.0990.0040.087-0.018-0.5140.0880.0170.0120.0050.0160.1250.0930.1350.071-0.0030.0990.0240.0250.0000.0770.1250.1190.0710.0990.1000.0090.0190.094-0.0100.106-0.0120.022-0.0030.0080.9300.0710.0720.100
Bodily_Injuries0.0710.0780.1060.0280.0530.0820.0090.0570.0580.0991.0000.1100.0700.0390.0070.0240.0000.0060.0000.0730.0360.0330.1150.0360.0000.0600.0670.0930.0000.0490.1500.0350.0360.0470.0460.0910.0690.0650.0000.0400.0090.1130.0060.0130.0640.0360.0640.055
Capital_Gains0.0830.090-0.0130.0880.095-0.0210.0100.0400.0400.0040.1101.000-0.0470.010-0.0160.0740.000-0.006-0.0110.0360.0600.0600.1510.091-0.0020.0850.0150.0410.0090.1120.1070.0810.0910.0910.120-0.019-0.0080.094-0.0040.093-0.006-0.046-0.0070.0160.0170.0910.0960.079
Capital_Loss0.0920.092-0.0320.0870.083-0.0020.005-0.045-0.0330.0870.070-0.0471.0000.009-0.0320.1010.000-0.0030.0010.0480.0570.0920.1410.0750.0050.1050.038-0.0010.0060.0780.1110.1030.0750.0860.115-0.0110.0300.1020.0090.1180.010-0.0170.012-0.0030.0820.0750.0940.097
Claim_Duration0.0730.0730.0350.0610.0490.017-0.000-0.0190.022-0.0180.0390.0100.0091.0000.0040.0570.0000.001-0.0070.0110.1040.2710.1280.0740.0080.0810.013-0.0270.0030.0320.1170.0430.0740.0470.017-0.039-0.0350.070-0.0010.0410.0040.0110.0010.006-0.0030.0740.0650.055
Claim_Intensity0.0140.0150.0400.0130.0130.0080.0030.048-0.074-0.5140.007-0.016-0.0320.0041.0000.0140.000-0.0100.003-0.0050.0210.0000.0190.0000.5030.022-0.015-0.0180.0050.0150.0200.0300.0000.0280.034-0.0110.0090.0210.5100.0100.799-0.0130.502-0.008-0.5560.0000.0210.018
Collision_Type0.0800.0950.2910.4260.5790.1140.0070.0820.0790.0880.0240.0740.1010.0570.0141.0000.0270.0050.0030.0940.1760.0700.1470.0710.0000.0660.0420.0430.0060.2350.1100.0630.0710.0440.0900.0840.0880.0560.0000.0360.0110.0720.0060.0300.0840.0710.0740.444
Commute_Discount0.0140.0070.0120.0370.0240.0000.0090.0140.0060.0170.0000.0000.0000.0000.0000.0271.0000.0000.0000.0000.0810.0080.0330.0000.0000.0090.0000.0060.0020.0100.0040.0000.0000.0020.0000.0030.0030.0000.0230.0000.0090.0020.0000.0000.0110.0000.0080.016
Customer_Life_Value10.0000.000-0.0070.0000.0000.001-0.006-0.0030.0020.0120.006-0.006-0.0030.001-0.0100.0050.0001.0000.006-0.0020.0000.0120.0000.005-0.0000.0000.004-0.0090.0150.0000.0000.0070.0050.0000.0040.0030.0010.0000.0000.000-0.0020.0040.002-0.0010.0130.0050.0000.010
DiffIN_Mileage0.0060.0000.0010.0150.0000.004-0.0030.0030.0030.0050.000-0.0110.001-0.0070.0030.0030.0000.0061.000-0.0030.0110.0120.0090.0000.0060.0000.0020.0020.0000.0000.0080.0040.0000.0080.0050.002-0.0020.0070.0030.0000.0050.0040.002-0.0050.0040.0000.0000.000
Education0.0720.085-0.0630.0740.079-0.0000.0050.0520.0490.0160.0730.0360.0480.011-0.0050.0940.000-0.002-0.0031.0000.0340.0720.1340.098-0.0030.0870.0200.0200.0000.0830.1250.0920.0980.0790.080-0.000-0.0200.0860.0040.0770.005-0.0150.0060.0090.0190.0980.0950.061
Fraud_Ind0.0480.1290.1500.5150.1720.0730.0000.0700.0930.1250.0360.0600.0570.1040.0210.1760.0810.0000.0110.0341.0000.1360.4050.0440.0000.0780.0890.1000.0000.0630.1300.0290.0440.0360.0340.1290.0940.0400.0020.0860.0000.1070.0100.0230.1170.0440.0880.169
Garage_Location0.0730.0430.0640.0990.0790.1170.0140.1200.1550.0930.0330.0600.0920.2710.0000.0700.0080.0120.0120.0720.1361.0000.1250.0330.0060.0430.0520.0610.0000.0660.0860.0330.0330.0590.0140.0980.0890.0250.0000.0280.0050.1830.0000.0350.0840.0330.0420.059
Hobbies0.1380.1520.1310.1370.1420.1300.0090.1440.1480.1350.1150.1510.1410.1280.0190.1470.0330.0000.0090.1340.4050.1251.0000.1330.0000.1410.1380.1330.0000.1300.1450.1690.1330.1090.1210.1210.1260.1490.0000.1140.0060.1330.0070.0500.1070.1330.1400.126
Indemnity_Policy_BI0.0620.0630.0790.0550.0560.0890.0090.0790.0880.0710.0360.0910.0750.0740.0000.0710.0000.0050.0000.0980.0440.0330.1331.0000.0080.0830.0340.0630.0000.0400.1250.0561.0000.0300.0350.1010.1270.0320.0060.0180.0000.0810.0000.0190.0681.0000.0590.078
Injury_Claim0.0000.0130.0010.0070.0000.0010.0050.004-0.008-0.0030.000-0.0020.0050.0080.5030.0000.000-0.0000.006-0.0030.0000.0060.0000.0081.0000.0000.001-0.0140.0000.0000.0080.0000.0080.0000.0000.0010.0040.0000.1440.0060.6010.0110.1480.006-0.0070.0080.0000.000
Insured_Relationship0.0760.0840.0970.0640.0590.0980.0000.0990.0960.0990.0600.0850.1050.0810.0220.0660.0090.0000.0000.0870.0780.0430.1410.0830.0001.0000.0590.0730.0000.0670.1260.0610.0830.0550.0930.0980.0720.0460.0000.0560.0090.1020.0050.0290.0950.0830.0640.078
Insured_Zip0.0810.0830.0130.0660.071-0.001-0.003-0.0290.0340.0240.0670.0150.0380.013-0.0150.0420.0000.0040.0020.0200.0890.0520.1380.0340.0010.0591.0000.0020.0000.0700.1380.0890.0340.0550.0720.0290.0400.0720.0030.072-0.0020.001-0.0080.0230.0230.0340.0510.056
License_Validity0.0870.0620.0380.0460.0490.0350.002-0.013-0.0370.0250.0930.041-0.001-0.027-0.0180.0430.006-0.0090.0020.0200.1000.0610.1330.063-0.0140.0730.0021.0000.0130.0700.1010.0820.0630.0910.0700.0250.0630.027-0.0020.095-0.005-0.0360.007-0.0020.0220.0630.0780.064
Low_Mileage_Discount0.0000.0000.0120.0040.0110.0000.9780.0100.0110.0000.0000.0090.0060.0030.0050.0060.0020.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0131.0000.0060.0040.0000.0000.0000.0080.0150.0000.0000.0000.0000.0110.0120.0110.0000.0080.0000.0070.000
Num_of_Vehicles_Involved0.0750.0830.1500.1770.5770.1210.0080.0830.1090.0770.0490.1120.0780.0320.0150.2350.0100.0000.0000.0830.0630.0660.1300.0400.0000.0670.0700.0700.0061.0000.1120.0510.0400.0740.1030.0920.0670.0440.0000.0400.0060.1260.0000.0200.0710.0400.0440.178
Occupation0.1050.1210.1030.1140.1170.1110.0000.1030.1100.1250.1500.1070.1110.1170.0200.1100.0040.0000.0080.1250.1300.0860.1450.1250.0080.1260.1380.1010.0040.1121.0000.1170.1250.1440.0960.1280.1200.0890.0070.0790.0040.1140.0000.0340.1010.1250.1160.105
Police_Report0.0710.0830.0860.0610.0590.0730.0000.0860.0970.1190.0350.0810.1030.0430.0300.0630.0000.0070.0040.0920.0290.0330.1690.0560.0000.0610.0890.0820.0000.0510.1171.0000.0560.0300.0480.1160.1070.0580.0110.0550.0090.1160.0100.0240.0930.0560.0450.056
Policy_BI0.0620.0630.0790.0550.0560.0890.0090.0790.0880.0710.0360.0910.0750.0740.0000.0710.0000.0050.0000.0980.0440.0330.1331.0000.0080.0830.0340.0630.0000.0400.1250.0561.0000.0300.0350.1010.1270.0320.0060.0180.0000.0810.0000.0190.0681.0000.0590.078
Policy_Ded0.0640.0830.1280.0250.0610.0870.0000.0900.0970.0990.0470.0910.0860.0470.0280.0440.0020.0000.0080.0790.0360.0590.1090.0300.0000.0550.0550.0910.0000.0740.1440.0300.0301.0000.0360.1000.1080.0420.0000.0220.0070.0940.0000.0240.0720.0300.0680.066
Policy_Duration0.0620.0550.0970.0650.1050.0720.0030.1180.1090.1000.0460.1200.1150.0170.0340.0900.0000.0040.0050.0800.0340.0140.1210.0350.0000.0930.0720.0700.0080.1030.0960.0480.0350.0361.0000.0800.0880.0370.0060.0500.0000.1000.0000.0170.0730.0350.0520.071
Policy_Num0.0850.1110.0340.0930.0960.0530.005-0.026-0.0310.0090.091-0.019-0.011-0.039-0.0110.0840.0030.0030.002-0.0000.1290.0980.1210.1010.0010.0980.0290.0250.0150.0920.1280.1160.1010.1000.0801.0000.0100.085-0.0020.076-0.001-0.006-0.002-0.0130.0240.1010.1040.104
Policy_Premium0.0950.095-0.0000.0760.0940.029-0.0040.010-0.0300.0190.069-0.0080.030-0.0350.0090.0880.0030.001-0.002-0.0200.0940.0890.1260.1270.0040.0720.0400.0630.0000.0670.1200.1070.1270.1080.0880.0101.0000.0880.0050.1110.006-0.0030.003-0.016-0.0060.1270.1020.097
Policy_State0.0820.0700.1020.0360.0430.0800.0000.1170.1020.0940.0650.0940.1020.0700.0210.0560.0000.0000.0070.0860.0400.0250.1490.0320.0000.0460.0720.0270.0000.0440.0890.0580.0320.0420.0370.0850.0881.0000.0120.0590.0000.0950.0070.0280.0920.0320.0340.046
Property_Claim0.0040.000-0.0030.0090.0000.0130.0080.001-0.011-0.0100.000-0.0040.009-0.0010.5100.0000.0230.0000.0030.0040.0020.0000.0000.0060.1440.0000.003-0.0020.0000.0000.0070.0110.0060.0000.006-0.0020.0050.0121.0000.0030.602-0.0030.1470.006-0.0140.0060.0060.006
Property_Damage0.1030.0610.0860.0840.0350.0880.0000.0820.0750.1060.0400.0930.1180.0410.0100.0360.0000.0000.0000.0770.0860.0280.1140.0180.0060.0560.0720.0950.0000.0400.0790.0550.0180.0220.0500.0760.1110.0590.0031.0000.0080.0960.0000.0170.0690.0180.0400.031
Total_Claim0.0030.000-0.0020.0040.0110.0100.0060.001-0.011-0.0120.009-0.0060.0100.0040.7990.0110.009-0.0020.0050.0050.0000.0050.0060.0000.6010.009-0.002-0.0050.0110.0060.0040.0090.0000.0070.000-0.0010.0060.0000.6020.0081.0000.0030.6020.004-0.0160.0000.0040.005
Umbrella_Limit0.1110.111-0.0150.1010.069-0.0040.0140.0020.0230.0220.113-0.046-0.0170.011-0.0130.0720.0020.0040.004-0.0150.1070.1830.1330.0810.0110.1020.001-0.0360.0120.1260.1140.1160.0810.0940.100-0.006-0.0030.095-0.0030.0960.0031.000-0.002-0.0060.0220.0810.0760.084
Vehicle_Claim0.0050.000-0.0050.0000.0000.0080.0030.007-0.003-0.0030.006-0.0070.0120.0010.5020.0060.0000.0020.0020.0060.0100.0000.0070.0000.1480.005-0.0080.0070.0110.0000.0000.0100.0000.0000.000-0.0020.0030.0070.1470.0000.602-0.0021.000-0.000-0.0040.0000.0000.008
Vehicle_Color0.0320.035-0.0080.0280.013-0.0160.002-0.0170.1180.0080.0130.016-0.0030.006-0.0080.0300.000-0.001-0.0050.0090.0230.0350.0500.0190.0060.0290.023-0.0020.0000.0200.0340.0240.0190.0240.017-0.013-0.0160.0280.0060.0170.004-0.006-0.0001.0000.0200.0190.0160.030
Vehicle_Cost0.0750.079-0.0750.0880.052-0.0020.003-0.0820.1140.9300.0640.0170.082-0.003-0.5560.0840.0110.0130.0040.0190.1170.0840.1070.068-0.0070.0950.0230.0220.0080.0710.1010.0930.0680.0720.0730.024-0.0060.092-0.0140.069-0.0160.022-0.0040.0201.0000.0680.0750.071
Wait_Policy_BI0.0620.0630.0790.0550.0560.0890.0090.0790.0880.0710.0360.0910.0750.0740.0000.0710.0000.0050.0000.0980.0440.0330.1331.0000.0080.0830.0340.0630.0000.0400.1250.0561.0000.0300.0350.1010.1270.0320.0060.0180.0000.0810.0000.0190.0681.0000.0590.078
Witnesses0.0950.0800.1100.0590.0470.1040.0080.0830.0790.0720.0640.0960.0940.0650.0210.0740.0080.0000.0000.0950.0880.0420.1400.0590.0000.0640.0510.0780.0070.0440.1160.0450.0590.0680.0520.1040.1020.0340.0060.0400.0040.0760.0000.0160.0750.0591.0000.061
authorities_contacted0.0890.0780.1990.3140.4470.0910.0000.0940.0930.1000.0550.0790.0970.0550.0180.4440.0160.0100.0000.0610.1690.0590.1260.0780.0000.0780.0560.0640.0000.1780.1050.0560.0780.0660.0710.1040.0970.0460.0060.0310.0050.0840.0080.0300.0710.0780.0611.000

Missing values

2025-07-25T19:22:04.000415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-25T19:22:04.431653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Claim_IDBind_Date1Customer_Life_Value1Age_InsuredPolicy_NumPolicy_StatePolicy_Start_DatePolicy_Expiry_DatePolicy_BIPolicy_DedPolicy_PremiumUmbrella_LimitInsured_ZipGenderEducationOccupationHobbiesInsured_RelationshipCapital_GainsCapital_LossGarage_LocationAccident_DateAccident_TypeCollision_TypeAccident_Severityauthorities_contactedAcccident_StateAcccident_CityAccident_LocationAccident_HourNum_of_Vehicles_InvolvedProperty_DamageBodily_InjuriesWitnessesPolice_ReportDL_Expiry_DateClaims_DateAuto_MakeAuto_ModelAuto_YearVehicle_ColorVehicle_CostAnnual_MileageDiffIN_MileageLow_Mileage_DiscountFraud_IndCommute_DiscountTotal_ClaimInjury_ClaimProperty_ClaimVehicle_ClaimVehicle_RegistrationCheck_PointWait_Policy_BIIndemnity_Policy_BIPolicy_DurationClaim_DurationLicense_ValidityClaim_Intensity
0AA000000012023-01-01122812379068702023-10-132024-04-13500/10001000986.53047272002adm-clericalpoloother-relative627000No2024-02-162120VAColumbus7819 Oak St11100212025-08-122024-02-17681550471509324990006162.56714.945370.7476.88IF47V1395No500100018315430.967605
1AA000000022023-01-01123112904447312023-10-212024-04-21250/5005001163.83060487400protective-servmovieshusband377000No2024-02-210202NCArlington7609 Rock St21422022026-04-152024-02-261560229115824424210020402.387669.315708.227024.85EI51L7783No25050018357841.428140
2AA000000032022-07-01185014686314902023-11-262024-05-26500/1000500889.13045942905priv-house-servboard-gamesother-relative00No2024-02-260123PANorthbend4618 Flute Ave14300202026-04-242024-03-0114923695410527234600010839.123646.113468.943724.07MU37B8905No500100018247880.446172
3AA000000042023-01-01123716310086912023-08-082024-02-08500/100010001060.74047158506tech-supportreadingown-child0-51500No2024-01-092100SCColumbus1229 5th Ave15122322026-03-172024-01-131119923723711603242501017423.885585.621863.469974.80RI52Q2108No500100018447980.710414
4AA000000052022-03-01222818558295802023-11-122024-05-12250/50010001235.14044356704exec-managerialcampinghusband0-32100No2024-02-170222OHHillsdale1643 Washington Hwy20310122025-11-012024-02-20132862251025982389010024527.387224.793074.1214228.47UX39O9355No25050018236231.619571
5AA000000062022-08-01173218906323212023-11-062024-05-06500/10005001592.41047432405prof-specialtyyachtinghusband58900-29100No2024-02-062120WVColumbus5771 Best St22112312025-12-162024-02-107369035702854167450002675.78112.042493.5470.20XK41H9013No500100018246790.115095
6AA000000072022-07-01184014535695522023-10-162024-04-16100/3005001463.95043056703salesskydivingown-child00No2024-02-110123NCSpringfield4545 4th Ridge20310012026-03-092024-02-1142720881817466683600019160.584890.493242.3911027.70MY30O4303No10030018307572.501907
7AA000000082022-02-01233219338448122023-10-142024-04-14250/5001000988.93061418702craft-repairgolfunmarried276000No2024-01-232022NYColumbus2889 Francis St11112312025-10-302024-01-264276423410942574460005644.45470.30317.064857.09II74U8175No25050018336460.389656
8AA000000092022-11-01143212014540312023-08-262024-02-26500/10005001612.43045676204other-serviceyachtingown-child364000No2024-01-082221VASpringfield2087 Apache Ave2112112025-06-112024-01-112090362788038660300017963.303941.907210.566810.84UD66G3260No500100018435200.757358
9AA000000102022-05-01204013367649612023-10-052024-04-05500/100010001361.45060483306handlers-cleanerscampingunmarried393000No2024-02-152110OHNorthbend7570 Cherokee Drive12120212025-11-122024-02-1911156225445634443531004589.871423.96362.222803.69QM79R9596No500100018346360.300274
Claim_IDBind_Date1Customer_Life_Value1Age_InsuredPolicy_NumPolicy_StatePolicy_Start_DatePolicy_Expiry_DatePolicy_BIPolicy_DedPolicy_PremiumUmbrella_LimitInsured_ZipGenderEducationOccupationHobbiesInsured_RelationshipCapital_GainsCapital_LossGarage_LocationAccident_DateAccident_TypeCollision_TypeAccident_Severityauthorities_contactedAcccident_StateAcccident_CityAccident_LocationAccident_HourNum_of_Vehicles_InvolvedProperty_DamageBodily_InjuriesWitnessesPolice_ReportDL_Expiry_DateClaims_DateAuto_MakeAuto_ModelAuto_YearVehicle_ColorVehicle_CostAnnual_MileageDiffIN_MileageLow_Mileage_DiscountFraud_IndCommute_DiscountTotal_ClaimInjury_ClaimProperty_ClaimVehicle_ClaimVehicle_RegistrationCheck_PointWait_Policy_BIIndemnity_Policy_BIPolicy_DurationClaim_DurationLicense_ValidityClaim_Intensity
39990AA000399912022-03-01223815653691012023-09-192024-03-19100/30020001540.19046384201adm-clericalskydivingother-relative0-74500No2024-02-012002VAColumbus5506 Best St20122002025-05-112024-02-01332431471517043279901017273.061711.761456.9614104.34CA53E2408No10030018204651.693598
39991AA000399922022-01-01244311837938502023-09-272024-03-27500/100010001387.51060939000salesbase-jumpingnot-in-family00No2024-01-110211NYRiverwood1102 Apache Hwy19321322025-08-162024-01-1473640155021454560880003916.901660.24179.372077.29IG87M9245No500100018235830.373081
39992AA000399932022-09-01164015393997802023-09-182024-03-18100/3005001238.65046870202transport-movingbungie-jumpinghusband0-44600No2024-01-273333WVSpringfield9744 Texas Drive5121102026-02-172024-01-281037230676805230270002065.00153.36294.761616.88PF04A5492No10030018217520.114457
39993AA000399942023-01-01122015051102322023-10-122024-04-12500/100010001189.98400000061332702craft-repairgolfother-relative0-54700No2024-02-012212WVColumbus1953 Sky Lane22101312026-02-262024-02-0115522031216860482800016640.555302.335124.676213.55UC61E1433No500100018307561.304235
39994AA000399952022-04-01213811129764822023-10-212024-04-21250/5005001405.71046537606craft-repaircampingunmarried00No2024-02-012123SCArlington9878 Washington Ave10110102025-05-292024-02-0113282274008127469400015247.399347.732966.372933.29EB57J1220No25050018304832.080791
39995AA000399962022-04-01214117294257812023-09-222024-03-22250/5001000817.28046026302salescross-fitunmarried622000No2024-01-030120SCColumbus4095 MLK St17311102025-11-202024-01-04824803250412705770101020237.8711144.278766.96326.64MW01M5376No25050018216871.013797
39996AA000399972022-03-01223517292372512023-08-112024-02-11500/100010001346.27046850805farming-fishingcross-fitnot-in-family44900-91400No2024-01-032020WVArlington6492 4th Lane11100222025-06-182024-01-0723772391611428938500006414.643827.301292.391294.95JL94U7211No500100018445320.168932
39997AA000399982022-09-01163615060546412023-09-202024-03-20500/100020001318.24900000060174801prof-specialtykayakingnot-in-family0-78600No2024-01-301334WVArlington5540 Sky St9100112025-08-132024-01-3042735121361162529590001569.7470.74497.391001.61CJ70W1494No500100018205610.175290
39998AA000399992022-06-01192611292857212023-10-032024-04-03250/50010001252.08046545601exec-managerialsleepingnot-in-family00No2024-02-080102VASpringfield6191 Oak Lane4222202025-09-262024-02-106850181608398264101016628.75534.449268.586825.73GI44P0705No25050018325961.385210
39999AA000400002022-01-01243515520348122023-10-122024-04-12500/100020001123.89046831304priv-house-servvideo-gamesunmarried35400-49200No2024-01-210021NYColumbus4119 Texas St0311322026-03-312024-01-231115953636698804627000534.66115.4068.86350.40TS11D6570No500100018328000.022470